05 - Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a93d11c8",
   "metadata": {},
   "source": [
    "# Progetto di Data and Web mining\n",
    "\n",
    "Matteo Spanio, A.A. 2021/2022\n",
    "\n",
    "# Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55c5aa07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Zillow.data as zd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "train, validation, test = zd.get_preprocessed_data()\n",
    "\n",
    "X_train, y_train = zd.split_x_y(train)\n",
    "X_validation, y_validation = zd.split_x_y(validation)\n",
    "X_test, y_test = zd.split_x_y(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43cadaa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 540 candidates, totalling 1620 fits\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   4.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.115, test=-0.115) total time=  17.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=   5.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=   9.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  14.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.071) total time=   5.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  15.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  20.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.073, test=-0.074) total time=  18.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   4.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=   9.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  10.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  12.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  10.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  12.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=   5.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.122, test=-0.121) total time=  23.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.073, test=-0.073) total time=  14.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.131, test=-0.131) total time=  24.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  16.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.080) total time=   6.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.151, test=-0.151) total time=  23.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   6.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=   7.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.115, test=-0.115) total time=  12.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.156, test=-0.155) total time=  24.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.074, test=-0.075) total time=   7.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.077, test=-0.078) total time=  14.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.094, test=-0.094) total time=  17.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.095, test=-0.095) total time=   7.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.123, test=-0.123) total time=  14.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.166, test=-0.166) total time=  24.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.096, test=-0.096) total time=   6.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.127, test=-0.126) total time=  12.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.141, test=-0.141) total time=  23.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.071) total time=   7.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.164, test=-0.163) total time=  18.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.103, test=-0.103) total time=  24.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.147, test=-0.146) total time=  18.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.102, test=-0.103) total time=  22.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.122, test=-0.122) total time=  12.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.081, test=-0.081) total time=   8.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=   9.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  10.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.082, test=-0.081) total time=   9.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.073, test=-0.073) total time=  17.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=   5.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.098, test=-0.098) total time=  14.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  10.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=   5.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.099, test=-0.099) total time=  14.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  14.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.081, test=-0.081) total time=  10.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.072, test=-0.073) total time=  17.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.071, test=-0.071) total time=   5.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  15.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.073, test=-0.073) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.072, test=-0.072) total time=  16.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   4.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.072, test=-0.073) total time=  10.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  11.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  14.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.103, test=-0.103) total time=  19.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.170, test=-0.169) total time=  28.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.168, test=-0.168) total time=  24.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.087, test=-0.087) total time=   7.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.130, test=-0.130) total time=  14.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.131, test=-0.131) total time=  25.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.071, test=-0.072) total time=   7.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.078, test=-0.079) total time=  13.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.094, test=-0.094) total time=  16.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.108, test=-0.107) total time=  20.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.156, test=-0.155) total time=  19.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.095, test=-0.095) total time=   7.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   7.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.103, test=-0.103) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.142, test=-0.142) total time=  38.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.166, test=-0.166) total time=  33.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.084, test=-0.085) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.147, test=-0.147) total time=  36.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.149, test=-0.149) total time=  32.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.072, test=-0.073) total time=  15.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   4.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.072, test=-0.073) total time=   8.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.123, test=-0.123) total time=  20.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.097, test=-0.096) total time=  14.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  12.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.081, test=-0.081) total time=   9.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.112, test=-0.111) total time=  18.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.072, test=-0.073) total time=  10.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  15.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   5.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.122, test=-0.121) total time=  21.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.091, test=-0.091) total time=  13.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.133, test=-0.133) total time=  25.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.126, test=-0.125) total time=  22.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  15.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  16.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.096, test=-0.096) total time=  18.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.108, test=-0.108) total time=  22.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.115, test=-0.115) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.148, test=-0.148) total time=  37.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.142, test=-0.142) total time=  32.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.100, test=-0.100) total time=  17.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.101, test=-0.100) total time=  14.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.072, test=-0.072) total time=   6.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.088, test=-0.088) total time=  11.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.087, test=-0.086) total time=  14.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.093, test=-0.092) total time=  17.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.090, test=-0.090) total time=  18.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.164, test=-0.164) total time=  22.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.081, test=-0.081) total time=  13.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.181, test=-0.181) total time=  28.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.087, test=-0.087) total time=  12.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.120, test=-0.120) total time=  26.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.071) total time=   7.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.093, test=-0.093) total time=  12.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.160, test=-0.160) total time=  29.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  14.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.131, test=-0.132) total time=  26.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.072, test=-0.073) total time=   9.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  14.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   4.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  13.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.127, test=-0.127) total time=  21.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.073, test=-0.074) total time=  17.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.070, test=-0.071) total time=   5.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  15.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.128, test=-0.128) total time=  23.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.124, test=-0.124) total time=  21.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.093, test=-0.093) total time=  13.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.129, test=-0.129) total time=  26.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.073, test=-0.073) total time=  16.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.081, test=-0.081) total time=  10.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.110, test=-0.109) total time=  17.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=   4.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.130, test=-0.129) total time=  21.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.088, test=-0.087) total time=  11.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.093, test=-0.092) total time=  14.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.103, test=-0.102) total time=  19.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.093, test=-0.092) total time=  15.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.149, test=-0.148) total time=  26.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.105, test=-0.105) total time=  19.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.173, test=-0.172) total time=  33.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.135, test=-0.135) total time=  25.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.080) total time=   6.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.148, test=-0.147) total time=  17.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.096, test=-0.095) total time=  19.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.124, test=-0.123) total time=  12.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.122, test=-0.122) total time=  25.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.069, test=-0.071) total time=   7.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.119, test=-0.118) total time=  12.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.118, test=-0.117) total time=  24.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.071, test=-0.072) total time=   7.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.079, test=-0.080) total time=  14.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.156, test=-0.156) total time=  26.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   7.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.133, test=-0.133) total time=  18.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.093, test=-0.092) total time=  17.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.084, test=-0.083) total time=   8.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.105, test=-0.106) total time=  22.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.073, test=-0.073) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.126, test=-0.126) total time=  20.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.070, test=-0.069) total time=  11.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  12.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  14.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.128, test=-0.127) total time=  24.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.073, test=-0.073) total time=  17.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.082, test=-0.082) total time=  10.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.072, test=-0.073) total time=  19.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   4.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  15.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  18.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.115, test=-0.115) total time=  18.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.073, test=-0.074) total time=   9.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  13.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  12.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  12.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.132, test=-0.132) total time=  25.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.138, test=-0.138) total time=  25.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   6.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.077, test=-0.078) total time=  14.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.084, test=-0.084) total time=  15.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.092, test=-0.091) total time=  15.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.150, test=-0.150) total time=  18.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.097, test=-0.096) total time=  18.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.078) total time=   7.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.083, test=-0.082) total time=  12.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.170, test=-0.169) total time=  28.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.110, test=-0.110) total time=  17.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.131, test=-0.131) total time=  19.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.081, test=-0.082) total time=  13.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.089, test=-0.088) total time=  19.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.087, test=-0.088) total time=   8.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.077) total time=   6.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.095, test=-0.095) total time=  18.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.151, test=-0.151) total time=  38.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.130, test=-0.130) total time=  31.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.093, test=-0.092) total time=  18.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.154, test=-0.154) total time=  23.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.115, test=-0.114) total time=  20.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.158, test=-0.157) total time=  30.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=   8.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.119, test=-0.119) total time=  19.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.093, test=-0.092) total time=  13.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  18.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.113, test=-0.113) total time=  17.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.073, test=-0.073) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  13.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  13.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.070, test=-0.069) total time=  12.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.127, test=-0.126) total time=  24.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  16.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.080, test=-0.080) total time=   9.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.072, test=-0.073) total time=  18.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.094, test=-0.094) total time=  12.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.070, test=-0.069) total time=  11.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.093, test=-0.093) total time=  14.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.070, test=-0.069) total time=  14.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.117, test=-0.116) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.159, test=-0.159) total time=  29.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.103, test=-0.102) total time=  13.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.187, test=-0.187) total time=  29.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.087, test=-0.086) total time=  13.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.168, test=-0.168) total time=  31.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.149, test=-0.149) total time=  19.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.150, test=-0.149) total time=  21.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.076, test=-0.077) total time=  12.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.162, test=-0.161) total time=  28.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.087, test=-0.086) total time=  12.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.161, test=-0.161) total time=  29.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.078, test=-0.079) total time=  13.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.092, test=-0.091) total time=  20.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.099, test=-0.099) total time=  18.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.108, test=-0.108) total time=  18.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.152, test=-0.151) total time=  27.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.153, test=-0.154) total time=  26.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   8.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.089, test=-0.090) total time=  14.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.133, test=-0.132) total time=  32.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.085, test=-0.084) total time=   8.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.093, test=-0.092) total time=  12.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.070, test=-0.070) total time=  11.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.080, test=-0.080) total time=   9.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.072, test=-0.072) total time=  14.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  13.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  13.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.070, test=-0.069) total time=  14.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  18.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.111, test=-0.111) total time=  17.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.071, test=-0.072) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.070, test=-0.070) total time=  14.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.070, test=-0.070) total time=   5.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.073, test=-0.074) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.071, test=-0.071) total time=  13.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  13.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  12.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  16.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.110, test=-0.109) total time=  18.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=   7.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.077, test=-0.079) total time=  13.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.155, test=-0.155) total time=  30.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.145, test=-0.144) total time=  17.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.115, test=-0.115) total time=  22.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   6.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.090, test=-0.090) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.143, test=-0.142) total time=  36.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.145, test=-0.144) total time=  29.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.095, test=-0.094) total time=  16.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.158, test=-0.158) total time=  34.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.146, test=-0.146) total time=  33.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.095, test=-0.095) total time=  15.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.199, test=-0.199) total time=  35.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.102, test=-0.103) total time=  18.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.100, test=-0.099) total time=  16.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.106, test=-0.106) total time=  19.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.140, test=-0.139) total time=  35.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.131, test=-0.130) total time=  27.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.074, test=-0.075) total time=   6.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.094, test=-0.094) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.069, test=-0.069) total time=   4.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  11.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  15.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.108, test=-0.107) total time=  16.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   4.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.072, test=-0.073) total time=  10.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.119, test=-0.118) total time=  21.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.120, test=-0.120) total time=  23.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.072, test=-0.073) total time=  14.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.072, test=-0.073) total time=  15.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.108, test=-0.108) total time=  19.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.070, test=-0.070) total time=  11.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.070, test=-0.070) total time=  10.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.070, test=-0.069) total time=  13.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.073, test=-0.073) total time=   5.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.070, test=-0.070) total time=  10.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.072, test=-0.073) total time=  16.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.086, test=-0.086) total time=   9.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=2, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.073, test=-0.073) total time=  19.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.071, test=-0.072) total time=   7.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.138, test=-0.137) total time=  19.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.144, test=-0.144) total time=  36.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.136, test=-0.137) total time=  31.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.145, test=-0.145) total time=  23.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.097, test=-0.097) total time=  19.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.090, test=-0.090) total time=  19.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.157, test=-0.156) total time=  33.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.147, test=-0.147) total time=  30.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.159, test=-0.158) total time=  25.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.122, test=-0.122) total time=  22.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.175, test=-0.174) total time=  25.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.104, test=-0.104) total time=   7.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   7.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.150, test=-0.149) total time=  17.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.095, test=-0.094) total time=  21.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.078, test=-0.079) total time=  13.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.091, test=-0.091) total time=  19.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.098, test=-0.097) total time=  18.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.123, test=-0.122) total time=  21.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.172, test=-0.172) total time=  39.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.131, test=-0.130) total time=  32.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.156, test=-0.155) total time=  23.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.094, test=-0.094) total time=   7.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.123, test=-0.123) total time=  12.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=4, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.127, test=-0.127) total time=  25.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   8.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.100, test=-0.100) total time=  14.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.143, test=-0.144) total time=  33.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.089, test=-0.090) total time=  14.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.152, test=-0.152) total time=  28.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.081, test=-0.082) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.098, test=-0.097) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.117, test=-0.116) total time=  26.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.162, test=-0.161) total time=  37.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.157, test=-0.157) total time=  39.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.128, test=-0.128) total time=  28.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.085, test=-0.086) total time=   7.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.085, test=-0.085) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.112, test=-0.112) total time=  18.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.139, test=-0.138) total time=  32.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.124, test=-0.122) total time=  18.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.151, test=-0.151) total time=  31.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.107, test=-0.107) total time=  18.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.157, test=-0.158) total time=  32.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.090, test=-0.089) total time=  13.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.141, test=-0.140) total time=  24.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=   8.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.094, test=-0.095) total time=  14.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.138, test=-0.136) total time=  29.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.175, test=-0.174) total time=  40.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.151, test=-0.151) total time=  33.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.130, test=-0.129) total time=  20.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.151, test=-0.150) total time=  33.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.116, test=-0.114) total time=  20.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.136, test=-0.135) total time=  34.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.099, test=-0.098) total time=  15.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.125, test=-0.125) total time=  26.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.100, test=-0.100) total time=  15.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.153, test=-0.153) total time=  27.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.154, test=-0.153) total time=  40.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.157, test=-0.158) total time=  35.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.095, test=-0.095) total time=  15.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   8.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.090, test=-0.090) total time=  13.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.121, test=-0.121) total time=  26.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.095, test=-0.095) total time=   8.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.087, test=-0.086) total time=   7.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.103, test=-0.104) total time=  23.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.118, test=-0.117) total time=  27.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.098, test=-0.099) total time=  20.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.145, test=-0.143) total time=  30.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.091, test=-0.092) total time=  14.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.148, test=-0.146) total time=  32.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.085, test=-0.087) total time=  14.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.128, test=-0.127) total time=  25.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.156, test=-0.156) total time=  37.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.152, test=-0.151) total time=  32.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.121, test=-0.121) total time=  19.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.152, test=-0.150) total time=  37.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.125, test=-0.124) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.172, test=-0.172) total time=  41.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.165, test=-0.163) total time=  32.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.097, test=-0.098) total time=  14.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.144, test=-0.143) total time=  26.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.136, test=-0.135) total time=  38.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.160, test=-0.162) total time=  35.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.100, test=-0.099) total time=  16.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.141, test=-0.141) total time=  27.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   8.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.094, test=-0.095) total time=  15.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.146, test=-0.148) total time=  28.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.089, test=-0.089) total time=   9.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.094, test=-0.093) total time=   8.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.096, test=-0.096) total time=  14.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.132, test=-0.131) total time=  28.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   8.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.088, test=-0.089) total time=  16.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.135, test=-0.134) total time=  28.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.078) total time=   9.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.093, test=-0.094) total time=  15.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.139, test=-0.141) total time=  30.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   8.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.098, test=-0.099) total time=  15.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.135, test=-0.134) total time=  27.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.121, test=-0.120) total time=  30.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.110, test=-0.109) total time=  19.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.152, test=-0.152) total time=  42.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.144, test=-0.142) total time=  32.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.097) total time=  12.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.142, test=-0.141) total time=  25.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.170, test=-0.170) total time=  34.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.141, test=-0.141) total time=  32.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.096, test=-0.095) total time=  14.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.137, test=-0.136) total time=  24.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.085, test=-0.085) total time=   7.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.078) total time=   7.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.108, test=-0.109) total time=  19.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.132, test=-0.131) total time=  38.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.134, test=-0.134) total time=  19.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.141, test=-0.141) total time=  37.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.125, test=-0.125) total time=  28.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=   8.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.115, test=-0.114) total time=  21.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.147, test=-0.145) total time=  31.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.096, test=-0.095) total time=  14.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.146, test=-0.147) total time=  28.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.079) total time=   8.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.111, test=-0.111) total time=  16.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.112, test=-0.111) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.179, test=-0.181) total time=  44.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.151, test=-0.150) total time=  33.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.092, test=-0.091) total time=   7.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.128, test=-0.130) total time=  22.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.159, test=-0.157) total time=  39.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.117, test=-0.117) total time=  20.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.217, test=-0.218) total time=  42.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.166, test=-0.165) total time=  33.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.075, test=-0.074) total time=   8.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.117, test=-0.116) total time=  21.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.158, test=-0.157) total time=  32.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.102, test=-0.102) total time=  14.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.116, test=-0.116) total time=  30.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.082) total time=  10.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.108, test=-0.109) total time=  18.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.149, test=-0.151) total time=  32.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=   8.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.102, test=-0.102) total time=  19.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.155, test=-0.154) total time=  36.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.119, test=-0.119) total time=  20.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.176, test=-0.177) total time=  41.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.147, test=-0.145) total time=  31.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.096, test=-0.095) total time=  13.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.129, test=-0.129) total time=  25.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.074, test=-0.074) total time=   7.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.095, test=-0.095) total time=  16.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.116, test=-0.117) total time=  27.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   8.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.098) total time=  13.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.118, test=-0.118) total time=  25.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.165, test=-0.164) total time=  35.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.133, test=-0.132) total time=  30.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.116, test=-0.115) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.150, test=-0.150) total time=  31.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.092, test=-0.092) total time=  14.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.123, test=-0.122) total time=  29.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.083, test=-0.084) total time=   8.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.092, test=-0.093) total time=  15.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.118, test=-0.118) total time=  25.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.086, test=-0.086) total time=   8.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.085, test=-0.084) total time=   9.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.099, test=-0.098) total time=  14.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.124, test=-0.123) total time=  28.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   8.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.101, test=-0.102) total time=  14.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.162, test=-0.160) total time=  29.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.157, test=-0.156) total time=  40.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.141, test=-0.141) total time=  35.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.111, test=-0.112) total time=  21.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.191, test=-0.188) total time=  38.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.122, test=-0.122) total time=  21.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.179, test=-0.181) total time=  42.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.136, test=-0.135) total time=  27.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.082, test=-0.083) total time=   8.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.105, test=-0.105) total time=  13.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.111, test=-0.110) total time=  21.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.199, test=-0.200) total time=  45.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.189, test=-0.187) total time=  36.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   8.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.118, test=-0.118) total time=  25.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.156, test=-0.155) total time=  37.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.144, test=-0.143) total time=  29.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.093, test=-0.092) total time=  12.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.143, test=-0.143) total time=  25.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.071, test=-0.072) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.106, test=-0.106) total time=  13.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.119, test=-0.118) total time=  22.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.145, test=-0.144) total time=  36.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.149, test=-0.149) total time=  32.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.096) total time=  14.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.133, test=-0.133) total time=  24.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.085, test=-0.085) total time=   6.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=   7.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.098, test=-0.097) total time=  15.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.139, test=-0.137) total time=  32.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.094, test=-0.094) total time=  13.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.114, test=-0.114) total time=  25.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.166, test=-0.165) total time=  35.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.182, test=-0.184) total time=  39.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.133, test=-0.134) total time=  24.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.183, test=-0.184) total time=  44.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.158, test=-0.156) total time=  35.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.107, test=-0.108) total time=  15.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.137, test=-0.139) total time=  30.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=   8.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.099, test=-0.099) total time=  14.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.127, test=-0.126) total time=  26.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.144, test=-0.143) total time=  39.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.161, test=-0.161) total time=  35.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.096) total time=  14.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.160, test=-0.163) total time=  28.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.087, test=-0.087) total time=   8.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.101, test=-0.100) total time=  15.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.149, test=-0.148) total time=  27.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.148, test=-0.147) total time=  39.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.146, test=-0.145) total time=  35.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.093, test=-0.092) total time=  18.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.145, test=-0.144) total time=  32.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   9.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.100, test=-0.101) total time=  16.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.156, test=-0.159) total time=  32.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.107, test=-0.106) total time=  20.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.150, test=-0.150) total time=  29.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.113, test=-0.113) total time=  18.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.129, test=-0.129) total time=  33.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.101, test=-0.100) total time=  20.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.140, test=-0.141) total time=  34.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.097, test=-0.098) total time=  19.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.133, test=-0.132) total time=  35.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.119, test=-0.118) total time=  22.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.156, test=-0.156) total time=  37.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.148, test=-0.146) total time=  31.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.078, test=-0.079) total time=  15.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.123, test=-0.122) total time=  27.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.073, test=-0.073) total time=   9.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.088, test=-0.089) total time=  13.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.122, test=-0.123) total time=  27.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   7.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.097) total time=  15.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.152, test=-0.154) total time=  29.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.083, test=-0.083) total time=   8.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.086, test=-0.085) total time=   7.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.131, test=-0.130) total time=  22.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.167, test=-0.169) total time=  37.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.117, test=-0.118) total time=  21.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.168, test=-0.166) total time=  40.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.121, test=-0.121) total time=  20.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.190, test=-0.191) total time=  44.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.183, test=-0.180) total time=  33.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.091, test=-0.092) total time=  15.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.164, test=-0.165) total time=  29.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.085, test=-0.085) total time=   7.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   9.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.129, test=-0.129) total time=  21.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.144, test=-0.143) total time=  35.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.127, test=-0.128) total time=  22.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.185, test=-0.183) total time=  41.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.133, test=-0.136) total time=  28.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   9.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.126, test=-0.128) total time=  26.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.214, test=-0.213) total time=  42.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.137, test=-0.135) total time=  23.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.122, test=-0.121) total time=  26.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.167, test=-0.166) total time=  36.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.143, test=-0.143) total time=  30.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.099, test=-0.098) total time=  14.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.132, test=-0.131) total time=  27.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.084, test=-0.084) total time=   7.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.085, test=-0.084) total time=   7.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.117, test=-0.116) total time=  19.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.159, test=-0.158) total time=  36.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.112, test=-0.111) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.137, test=-0.137) total time=  39.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.138, test=-0.136) total time=  32.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.094, test=-0.094) total time=  14.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.116, test=-0.116) total time=  26.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.093, test=-0.093) total time=  17.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.131, test=-0.131) total time=  26.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.081, test=-0.082) total time=   7.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.084, test=-0.084) total time=   7.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.107, test=-0.108) total time=  21.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.136, test=-0.135) total time=  37.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.113, test=-0.112) total time=  21.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.202, test=-0.204) total time=  41.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.181, test=-0.179) total time=  33.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.094, test=-0.095) total time=  15.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.147, test=-0.145) total time=  26.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.151, test=-0.151) total time=  38.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.149, test=-0.151) total time=  36.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.115, test=-0.114) total time=  20.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.179, test=-0.181) total time=  36.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.116, test=-0.116) total time=  21.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.131, test=-0.129) total time=  35.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.118, test=-0.120) total time=  22.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.185, test=-0.183) total time=  39.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.112, test=-0.111) total time=  20.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.184, test=-0.185) total time=  44.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.151, test=-0.149) total time=  35.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.105, test=-0.105) total time=  16.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.157, test=-0.157) total time=  30.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.180, test=-0.180) total time=  41.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.148, test=-0.149) total time=  36.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.132, test=-0.132) total time=  22.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.085, test=-0.086) total time=  13.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.113, test=-0.114) total time=  29.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.071, test=-0.072) total time=   7.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.082, test=-0.083) total time=  13.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.120, test=-0.121) total time=  27.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=   7.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.084, test=-0.084) total time=   7.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.127, test=-0.127) total time=  20.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.147, test=-0.146) total time=  30.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.127, test=-0.127) total time=  19.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.168, test=-0.166) total time=  36.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.108, test=-0.107) total time=  18.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.140, test=-0.140) total time=  38.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.132, test=-0.132) total time=  25.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.162, test=-0.161) total time=  36.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=6, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.143, test=-0.142) total time=  30.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.125, test=-0.127) total time=  22.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.177, test=-0.175) total time=  38.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.114, test=-0.113) total time=  21.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.174, test=-0.174) total time=  39.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.148, test=-0.149) total time=  31.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.080) total time=   8.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.078) total time=   7.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.143, test=-0.145) total time=  24.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.162, test=-0.160) total time=  36.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.114, test=-0.114) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.188, test=-0.189) total time=  43.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.176, test=-0.174) total time=  35.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  14.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.134, test=-0.133) total time=  26.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.152, test=-0.152) total time=  37.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.208, test=-0.210) total time=  35.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.103, test=-0.103) total time=  19.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.159, test=-0.161) total time=  38.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.130, test=-0.129) total time=  25.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.169, test=-0.172) total time=  37.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.143, test=-0.145) total time=  25.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.228, test=-0.226) total time=  43.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.197, test=-0.195) total time=  29.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.178, test=-0.177) total time=  46.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.178, test=-0.181) total time=  42.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.133, test=-0.135) total time=  26.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.152, test=-0.151) total time=  44.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.159, test=-0.158) total time=  36.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.099, test=-0.098) total time=  14.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.135, test=-0.134) total time=  27.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   8.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   8.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.093, test=-0.092) total time=  14.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.142, test=-0.141) total time=  27.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=  10.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.077, test=-0.076) total time=   9.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.142, test=-0.141) total time=  25.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.157, test=-0.157) total time=  41.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.152, test=-0.152) total time=  23.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.155, test=-0.155) total time=  37.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.132, test=-0.132) total time=  22.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.200, test=-0.202) total time=  36.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.135, test=-0.137) total time=  23.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.202, test=-0.200) total time=  39.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.160, test=-0.159) total time=  28.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.221, test=-0.221) total time=  42.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.182, test=-0.185) total time=  39.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.141, test=-0.144) total time=  25.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.220, test=-0.218) total time=  44.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.118, test=-0.118) total time=  25.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.210, test=-0.213) total time=  47.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.239, test=-0.237) total time=  35.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   9.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.158, test=-0.157) total time=  23.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.186, test=-0.185) total time=  38.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.153, test=-0.153) total time=  22.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.207, test=-0.205) total time=  34.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.115, test=-0.117) total time=  17.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.156, test=-0.159) total time=  32.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.082, test=-0.083) total time=  10.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.113, test=-0.115) total time=  17.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.210, test=-0.214) total time=  32.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.083, test=-0.084) total time=  10.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.105, test=-0.107) total time=  18.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.145, test=-0.147) total time=  33.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.083, test=-0.084) total time=  10.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.086, test=-0.086) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.095, test=-0.094) total time=  16.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.164, test=-0.163) total time=  34.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   8.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.107, test=-0.108) total time=  22.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=8, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.186, test=-0.182) total time=  43.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.115, test=-0.115) total time=  25.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.199, test=-0.201) total time=  42.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.174, test=-0.173) total time=  34.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.111, test=-0.112) total time=  15.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.110, test=-0.109) total time=  21.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.217, test=-0.219) total time=  47.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.160, test=-0.158) total time=  32.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   9.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.107, test=-0.107) total time=  18.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.148, test=-0.147) total time=  30.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.176, test=-0.176) total time=  43.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.208, test=-0.207) total time=  38.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.109, test=-0.109) total time=  22.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.197, test=-0.199) total time=  42.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.132, test=-0.132) total time=  27.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  10.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.094, test=-0.096) total time=  17.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.170, test=-0.169) total time=  29.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.180, test=-0.179) total time=  42.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.186, test=-0.187) total time=  39.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.110, test=-0.109) total time=  16.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.169, test=-0.170) total time=  32.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=   9.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.111, test=-0.113) total time=  17.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.183, test=-0.185) total time=  33.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  10.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.096, test=-0.097) total time=  16.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.126, test=-0.126) total time=  24.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.225, test=-0.227) total time=  45.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.231, test=-0.227) total time=  37.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.076) total time=   9.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.171, test=-0.174) total time=  26.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.217, test=-0.216) total time=  45.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.205, test=-0.204) total time=  30.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.188, test=-0.188) total time=  45.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.170, test=-0.170) total time=  42.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.166, test=-0.170) total time=  27.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.298, test=-0.295) total time=  47.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.218, test=-0.221) total time=  47.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.181, test=-0.181) total time=  36.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.084, test=-0.085) total time=  19.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.160, test=-0.162) total time=  35.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=   9.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.106, test=-0.106) total time=  18.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.172, test=-0.174) total time=  30.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   8.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   8.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.098, test=-0.098) total time=  15.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.147, test=-0.145) total time=  28.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.078) total time=   9.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.100, test=-0.101) total time=  16.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.156, test=-0.159) total time=  29.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.084, test=-0.085) total time=   8.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.080) total time=   8.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.099, test=-0.099) total time=  15.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.167, test=-0.166) total time=  34.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.084, test=-0.084) total time=   8.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.093, test=-0.092) total time=  15.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.189, test=-0.187) total time=  36.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.105, test=-0.106) total time=  17.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.119, test=-0.118) total time=  22.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.215, test=-0.218) total time=  45.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.236, test=-0.234) total time=  36.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.143, test=-0.142) total time=  23.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.230, test=-0.234) total time=  39.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.162, test=-0.165) total time=  25.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.248, test=-0.245) total time=  44.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.131, test=-0.130) total time=  23.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.224, test=-0.226) total time=  46.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.206, test=-0.204) total time=  38.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.115, test=-0.115) total time=  16.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.142, test=-0.141) total time=  23.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.236, test=-0.240) total time=  49.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.227, test=-0.225) total time=  39.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.105, test=-0.105) total time=  18.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.152, test=-0.152) total time=  32.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.223, test=-0.222) total time=  47.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.214, test=-0.217) total time=  44.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.105, test=-0.106) total time=  18.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.108, test=-0.108) total time=  16.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.164, test=-0.164) total time=  29.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.189, test=-0.188) total time=  42.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.159, test=-0.158) total time=  38.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.093, test=-0.093) total time=  17.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.176, test=-0.174) total time=  39.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.084, test=-0.083) total time=   9.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.110, test=-0.109) total time=  16.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.201, test=-0.199) total time=  36.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.099, test=-0.099) total time=  15.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.113, test=-0.112) total time=  21.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.216, test=-0.218) total time=  41.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.122, test=-0.121) total time=  26.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=   8.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.103, test=-0.104) total time=  14.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.108, test=-0.107) total time=  21.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.199, test=-0.201) total time=  43.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.139, test=-0.138) total time=  28.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.076) total time=  10.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.104, test=-0.106) total time=  18.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.197, test=-0.200) total time=  35.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=   9.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.113, test=-0.113) total time=  16.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.190, test=-0.189) total time=  32.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.225, test=-0.224) total time=  43.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.204, test=-0.205) total time=  35.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.092, test=-0.093) total time=  16.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.242, test=-0.240) total time=  37.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   9.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.164, test=-0.167) total time=  24.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.243, test=-0.242) total time=  43.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.126, test=-0.126) total time=  24.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.228, test=-0.231) total time=  45.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.137, test=-0.135) total time=  30.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.117, test=-0.117) total time=  16.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.196, test=-0.194) total time=  35.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.205, test=-0.204) total time=  50.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.161, test=-0.161) total time=  43.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.161, test=-0.164) total time=  27.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.257, test=-0.255) total time=  48.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.241, test=-0.239) total time=  32.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.150, test=-0.150) total time=  22.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.200, test=-0.203) total time=  37.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.151, test=-0.154) total time=  24.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.214, test=-0.212) total time=  43.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.118, test=-0.117) total time=  23.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.200, test=-0.201) total time=  49.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.146, test=-0.146) total time=  30.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=   8.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.095, test=-0.096) total time=  16.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.132, test=-0.134) total time=  30.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.084, test=-0.085) total time=   8.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   8.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.096, test=-0.096) total time=  16.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.184, test=-0.183) total time=  34.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.101, test=-0.102) total time=  16.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.175, test=-0.177) total time=  30.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.083, test=-0.083) total time=   9.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  16.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.131, test=-0.130) total time=  23.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.212, test=-0.214) total time=  45.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.235, test=-0.233) total time=  38.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.080) total time=   9.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.173, test=-0.175) total time=  25.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.259, test=-0.257) total time=  42.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.146, test=-0.146) total time=  23.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.255, test=-0.258) total time=  44.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.168, test=-0.169) total time=  30.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   9.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.113, test=-0.113) total time=  16.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.169, test=-0.168) total time=  31.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.216, test=-0.216) total time=  44.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.171, test=-0.169) total time=  39.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.127, test=-0.127) total time=  22.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.182, test=-0.182) total time=  37.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.157, test=-0.157) total time=  23.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.241, test=-0.243) total time=  43.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.140, test=-0.140) total time=  23.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.186, test=-0.188) total time=  43.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.172, test=-0.171) total time=  28.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.238, test=-0.239) total time=  44.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.148, test=-0.152) total time=  27.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.295, test=-0.291) total time=  48.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.147, test=-0.150) total time=  39.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.130, test=-0.132) total time=  26.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.231, test=-0.228) total time=  44.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.117, test=-0.117) total time=  23.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.198, test=-0.199) total time=  44.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.193, test=-0.191) total time=  35.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.099, test=-0.100) total time=  15.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.185, test=-0.184) total time=  29.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.192, test=-0.192) total time=  42.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.152, test=-0.152) total time=  35.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.105, test=-0.106) total time=  22.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.180, test=-0.180) total time=  36.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.103, test=-0.103) total time=  17.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.155, test=-0.155) total time=  31.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=   9.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.107, test=-0.108) total time=  17.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.157, test=-0.158) total time=  34.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.107, test=-0.108) total time=  15.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.166, test=-0.165) total time=  29.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.231, test=-0.229) total time=  42.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.235, test=-0.239) total time=  41.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.092, test=-0.091) total time=  16.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.148, test=-0.147) total time=  33.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.082) total time=   9.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  16.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.143, test=-0.144) total time=  31.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=   9.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.085, test=-0.084) total time=   9.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.178, test=-0.181) total time=  23.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.237, test=-0.235) total time=  42.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.134, test=-0.134) total time=  26.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.244, test=-0.247) total time=  49.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.177, test=-0.176) total time=  32.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=   9.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.103, test=-0.103) total time=  18.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.208, test=-0.207) total time=  34.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.203, test=-0.203) total time=  49.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.202, test=-0.206) total time=  43.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.095, test=-0.094) total time=  18.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.180, test=-0.179) total time=  36.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=  10.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.075, test=-0.076) total time=   8.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.092, test=-0.093) total time=  16.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.158, test=-0.160) total time=  32.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.083, test=-0.084) total time=   9.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.105, test=-0.105) total time=  17.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.167, test=-0.166) total time=  31.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.169, test=-0.168) total time=  47.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.195, test=-0.196) total time=  38.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.121, test=-0.123) total time=  22.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.177, test=-0.177) total time=  35.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.140, test=-0.140) total time=  21.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.211, test=-0.213) total time=  35.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.125, test=-0.124) total time=  21.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.216, test=-0.219) total time=  37.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.139, test=-0.143) total time=  25.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.201, test=-0.199) total time=  44.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.174, test=-0.173) total time=  29.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.199, test=-0.199) total time=  47.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.192, test=-0.194) total time=  41.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.149, test=-0.152) total time=  25.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.249, test=-0.247) total time=  42.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.203, test=-0.201) total time=  31.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.206, test=-0.204) total time=  42.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.226, test=-0.229) total time=  40.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.165, test=-0.164) total time=  22.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.201, test=-0.204) total time=  40.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.101, test=-0.100) total time=  15.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.225, test=-0.223) total time=  35.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.080, test=-0.079) total time=   9.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.160, test=-0.162) total time=  26.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.232, test=-0.229) total time=  47.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.137, test=-0.136) total time=  26.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.211, test=-0.213) total time=  50.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.182, test=-0.182) total time=  34.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=  10.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.111, test=-0.112) total time=  20.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.199, test=-0.203) total time=  37.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=  10.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.113, test=-0.115) total time=  19.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.176, test=-0.179) total time=  37.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=  10.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  11.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.191, test=-0.188) total time=  42.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.163, test=-0.162) total time=  29.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.189, test=-0.189) total time=  41.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.153, test=-0.152) total time=  36.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.152, test=-0.155) total time=  24.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.202, test=-0.201) total time=  40.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=10, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.144, test=-0.146) total time=  30.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   9.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.139, test=-0.139) total time=  24.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.202, test=-0.203) total time=  39.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.156, test=-0.155) total time=  23.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.175, test=-0.174) total time=  37.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.095, test=-0.094) total time=  16.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.165, test=-0.164) total time=  30.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.081) total time=   9.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.129, test=-0.131) total time=  18.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.182, test=-0.184) total time=  33.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=   9.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.114, test=-0.115) total time=  17.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.169, test=-0.172) total time=  32.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=   9.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.108, test=-0.108) total time=  16.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.213, test=-0.210) total time=  30.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.203, test=-0.202) total time=  43.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.198, test=-0.201) total time=  38.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.107, test=-0.107) total time=  18.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.169, test=-0.169) total time=  35.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.079) total time=  11.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.117, test=-0.119) total time=  19.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.188, test=-0.191) total time=  35.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=  10.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.113, test=-0.116) total time=  20.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.217, test=-0.221) total time=  37.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=  10.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.101, test=-0.102) total time=  18.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.131, test=-0.130) total time=  28.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.259, test=-0.263) total time=  54.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.210, test=-0.209) total time=  43.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.113, test=-0.113) total time=  21.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.207, test=-0.210) total time=  37.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=  10.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=12, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.233, test=-0.231) total time=  39.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.074, test=-0.074) total time=  10.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.108, test=-0.108) total time=  18.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.243, test=-0.241) total time=  41.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.075, test=-0.074) total time=  10.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.203, test=-0.201) total time=  25.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.228, test=-0.228) total time=  41.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.146, test=-0.145) total time=  27.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.180, test=-0.180) total time=  47.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.166, test=-0.168) total time=  30.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.244, test=-0.242) total time=  47.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.143, test=-0.143) total time=  27.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.256, test=-0.258) total time=  56.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.248, test=-0.246) total time=  44.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.119, test=-0.119) total time=  17.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.221, test=-0.219) total time=  35.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.233, test=-0.234) total time=  54.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.201, test=-0.200) total time=  40.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.162, test=-0.160) total time=  24.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.208, test=-0.211) total time=  42.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.168, test=-0.168) total time=  26.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.274, test=-0.276) total time=  41.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.108, test=-0.108) total time=  18.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.207, test=-0.207) total time=  32.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  11.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.114, test=-0.116) total time=  20.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.222, test=-0.226) total time=  36.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=  10.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.097, test=-0.097) total time=  18.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.148, test=-0.147) total time=  28.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.244, test=-0.247) total time=  49.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.256, test=-0.253) total time=  38.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.103, test=-0.103) total time=  18.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.142, test=-0.141) total time=  26.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.274, test=-0.277) total time=  50.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.279, test=-0.278) total time=  37.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=   9.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.106, test=-0.107) total time=  17.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.245, test=-0.242) total time=  40.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.090, test=-0.089) total time=   9.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.097, test=-0.098) total time=  18.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.204, test=-0.203) total time=  32.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.146, test=-0.145) total time=  28.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.263, test=-0.265) total time=  50.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.194, test=-0.194) total time=  32.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.084, test=-0.084) total time=  10.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.129, test=-0.127) total time=  19.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.200, test=-0.198) total time=  35.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.239, test=-0.238) total time=  50.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.233, test=-0.236) total time=  43.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.106, test=-0.106) total time=  17.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.147, test=-0.148) total time=  38.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.073, test=-0.074) total time=  12.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.130, test=-0.132) total time=  21.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.179, test=-0.183) total time=  37.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=  11.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.140, test=-0.143) total time=  27.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.268, test=-0.267) total time=  50.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.153, test=-0.151) total time=  26.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.263, test=-0.266) total time=  50.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.182, test=-0.181) total time=  34.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.076) total time=  11.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.116, test=-0.118) total time=  19.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.189, test=-0.193) total time=  34.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=   9.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.082, test=-0.082) total time=  10.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.177, test=-0.179) total time=  28.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.321, test=-0.319) total time=  46.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.153, test=-0.153) total time=  26.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.279, test=-0.283) total time=  50.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.213, test=-0.214) total time=  34.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=   9.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.110, test=-0.113) total time=  19.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.236, test=-0.234) total time=  33.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.234, test=-0.235) total time=  46.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.253, test=-0.256) total time=  44.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.161, test=-0.159) total time=  25.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.271, test=-0.275) total time=  45.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.192, test=-0.194) total time=  27.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.247, test=-0.246) total time=  46.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.143, test=-0.142) total time=  26.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.284, test=-0.288) total time=  53.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.295, test=-0.293) total time=  40.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.211, test=-0.210) total time=  51.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.235, test=-0.238) total time=  44.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  20.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.177, test=-0.176) total time=  38.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.084, test=-0.085) total time=  10.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.107, test=-0.108) total time=  18.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.184, test=-0.185) total time=  37.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=  10.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.082, test=-0.081) total time=  11.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.158, test=-0.161) total time=  31.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.245, test=-0.243) total time=  50.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.149, test=-0.149) total time=  29.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.236, test=-0.239) total time=  55.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.248, test=-0.246) total time=  40.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.109, test=-0.109) total time=  17.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.189, test=-0.189) total time=  34.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.237, test=-0.237) total time=  45.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.252, test=-0.255) total time=  42.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.195, test=-0.193) total time=  24.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.228, test=-0.233) total time=  42.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.120, test=-0.120) total time=  19.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.267, test=-0.263) total time=  40.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=   9.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.172, test=-0.175) total time=  28.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.278, test=-0.275) total time=  51.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.196, test=-0.200) total time=  36.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.083, test=-0.083) total time=  10.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.101, test=-0.102) total time=  18.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.276, test=-0.273) total time=  40.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.129, test=-0.128) total time=  17.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.155, test=-0.154) total time=  23.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.263, test=-0.268) total time=  49.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.191, test=-0.194) total time=  36.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=  10.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.128, test=-0.131) total time=  20.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.187, test=-0.190) total time=  37.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.082) total time=  12.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.138, test=-0.141) total time=  20.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.210, test=-0.213) total time=  36.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=  11.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.140, test=-0.140) total time=  18.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.111, test=-0.111) total time=  20.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.210, test=-0.209) total time=  33.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.211, test=-0.210) total time=  48.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.224, test=-0.223) total time=  43.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.096, test=-0.096) total time=  21.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.176, test=-0.175) total time=  36.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.077) total time=  11.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.108, test=-0.110) total time=  20.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.176, test=-0.174) total time=  34.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=  11.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=  12.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.190, test=-0.194) total time=  30.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.257, test=-0.254) total time=  46.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.143, test=-0.143) total time=  25.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.246, test=-0.249) total time=  48.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.270, test=-0.268) total time=  42.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.117, test=-0.118) total time=  17.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.152, test=-0.152) total time=  25.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.273, test=-0.277) total time=  53.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.185, test=-0.184) total time=  33.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.073, test=-0.075) total time=  11.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.117, test=-0.119) total time=  19.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.224, test=-0.227) total time=  34.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  10.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.114, test=-0.114) total time=  16.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.166, test=-0.166) total time=  27.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.253, test=-0.256) total time=  49.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.201, test=-0.200) total time=  31.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.081, test=-0.081) total time=   9.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.106, test=-0.106) total time=  20.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.173, test=-0.172) total time=  34.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=   9.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.112, test=-0.113) total time=  17.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.230, test=-0.228) total time=  32.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.229, test=-0.229) total time=  47.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.222, test=-0.226) total time=  40.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.121, test=-0.121) total time=  19.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.188, test=-0.187) total time=  33.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.079) total time=  10.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.108, test=-0.110) total time=  18.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.245, test=-0.246) total time=  35.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.151, test=-0.150) total time=  26.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.266, test=-0.269) total time=  54.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.271, test=-0.268) total time=  43.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=   9.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.167, test=-0.170) total time=  28.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.265, test=-0.263) total time=  50.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.155, test=-0.154) total time=  27.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.237, test=-0.239) total time=  53.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.214, test=-0.217) total time=  38.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.083, test=-0.084) total time=  10.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.108, test=-0.110) total time=  18.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.193, test=-0.195) total time=  38.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  11.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=   9.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.110, test=-0.109) total time=  18.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.250, test=-0.249) total time=  39.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  10.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.164, test=-0.167) total time=  28.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.311, test=-0.308) total time=  50.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.194, test=-0.197) total time=  34.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  10.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  10.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.189, test=-0.188) total time=  26.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.235, test=-0.238) total time=  39.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.116, test=-0.116) total time=  19.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.260, test=-0.258) total time=  40.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=   9.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.187, test=-0.185) total time=  25.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.221, test=-0.225) total time=  45.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.165, test=-0.165) total time=  25.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.223, test=-0.223) total time=  42.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.166, test=-0.169) total time=  28.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.291, test=-0.289) total time=  48.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.165, test=-0.165) total time=  24.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.254, test=-0.258) total time=  51.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.261, test=-0.260) total time=  42.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.128, test=-0.128) total time=  18.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.276, test=-0.275) total time=  32.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.277, test=-0.277) total time=  48.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.297, test=-0.295) total time=  39.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.133, test=-0.133) total time=  18.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.276, test=-0.274) total time=  33.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.246, test=-0.244) total time=  41.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.078) total time=  11.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.160, test=-0.162) total time=  29.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.300, test=-0.297) total time=  53.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.149, test=-0.148) total time=  27.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.222, test=-0.225) total time=  49.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.245, test=-0.243) total time=  43.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.124, test=-0.125) total time=  19.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.234, test=-0.233) total time=  37.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.241, test=-0.242) total time=  51.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.212, test=-0.213) total time=  44.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.112, test=-0.112) total time=  20.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.165, test=-0.164) total time=  34.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.078, test=-0.079) total time=  10.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.127, test=-0.130) total time=  19.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.198, test=-0.202) total time=  32.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  10.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.127, test=-0.126) total time=  18.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.204, test=-0.202) total time=  32.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.261, test=-0.262) total time=  51.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.206, test=-0.205) total time=  43.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.186, test=-0.189) total time=  28.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.290, test=-0.287) total time=  48.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.222, test=-0.222) total time=  35.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.255, test=-0.255) total time=  51.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.284, test=-0.282) total time=  47.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.207, test=-0.205) total time=  33.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.255, test=-0.254) total time=  48.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.236, test=-0.237) total time=  42.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.177, test=-0.175) total time=  26.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.234, test=-0.237) total time=  45.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.141, test=-0.140) total time=  26.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.216, test=-0.215) total time=  41.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.192, test=-0.196) total time=  28.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.334, test=-0.332) total time=  49.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.212, test=-0.214) total time=  37.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  12.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.113, test=-0.115) total time=  20.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.229, test=-0.233) total time=  38.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.080) total time=  11.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.121, test=-0.124) total time=  20.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.191, test=-0.191) total time=  31.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.169, test=-0.169) total time=  26.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.219, test=-0.218) total time=  44.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.149, test=-0.149) total time=  27.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.195, test=-0.198) total time=  44.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.175, test=-0.174) total time=  27.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.197, test=-0.197) total time=  45.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.113, test=-0.113) total time=  20.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.223, test=-0.222) total time=  43.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.096, test=-0.097) total time=  18.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.197, test=-0.196) total time=  36.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.230, test=-0.229) total time=  49.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.205, test=-0.205) total time=  38.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.110, test=-0.110) total time=  19.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.198, test=-0.198) total time=  34.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.074, test=-0.075) total time=  10.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.113, test=-0.116) total time=  19.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.225, test=-0.224) total time=  34.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.259, test=-0.259) total time=  47.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.250, test=-0.250) total time=  40.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.192, test=-0.191) total time=  24.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.216, test=-0.215) total time=  44.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.199, test=-0.202) total time=  31.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.216, test=-0.216) total time=  45.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.133, test=-0.136) total time=  28.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.250, test=-0.248) total time=  47.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.222, test=-0.226) total time=  38.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.074, test=-0.075) total time=  10.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.124, test=-0.127) total time=  18.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.188, test=-0.188) total time=  34.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.085, test=-0.085) total time=   9.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.113, test=-0.112) total time=  17.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.237, test=-0.233) total time=  31.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.243, test=-0.244) total time=  48.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.257, test=-0.261) total time=  41.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.106, test=-0.106) total time=  20.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.207, test=-0.206) total time=  34.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  10.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  10.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.203, test=-0.207) total time=  29.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.329, test=-0.327) total time=  46.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.186, test=-0.187) total time=  25.6s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.280, test=-0.283) total time=  52.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.100, test=-0.101) total time=  16.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.214, test=-0.213) total time=  35.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.210, test=-0.209) total time=  51.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.253, test=-0.256) total time=  46.7s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.139, test=-0.139) total time=  26.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.246, test=-0.249) total time=  46.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.155, test=-0.157) total time=  27.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.212, test=-0.216) total time=  44.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.160, test=-0.163) total time=  25.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=14, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.275, test=-0.273) total time=  47.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.141, test=-0.141) total time=  25.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.250, test=-0.254) total time=  51.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.264, test=-0.262) total time=  42.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.117, test=-0.119) total time=  17.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.220, test=-0.219) total time=  34.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.239, test=-0.238) total time=  46.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.221, test=-0.224) total time=  44.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.102, test=-0.102) total time=  19.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.191, test=-0.192) total time=  33.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.077, test=-0.078) total time=  11.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.126, test=-0.128) total time=  20.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.199, test=-0.202) total time=  37.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.091, test=-0.093) total time=  10.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.124, test=-0.126) total time=  19.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.202, test=-0.202) total time=  34.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.241, test=-0.240) total time=  50.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.249, test=-0.249) total time=  41.8s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.144, test=-0.146) total time=  29.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=16, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.280, test=-0.277) total time=  45.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.178, test=-0.179) total time=  30.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.255, test=-0.260) total time=  54.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.247, test=-0.250) total time=  45.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.220, test=-0.219) total time=  26.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.226, test=-0.227) total time=  42.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.112, test=-0.112) total time=  18.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.253, test=-0.251) total time=  40.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.110, test=-0.110) total time=  18.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.167, test=-0.167) total time=  26.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.274, test=-0.277) total time=  53.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=50;, score=(train=-0.300, test=-0.298) total time=  39.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=  10.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.100, test=-0.100) total time=  18.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=  10.0s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.083, test=-0.082) total time=  11.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.218, test=-0.216) total time=  25.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.232, test=-0.232) total time=  43.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.158, test=-0.161) total time=  27.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.320, test=-0.317) total time=  47.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.153, test=-0.153) total time=  24.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.272, test=-0.277) total time=  52.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.218, test=-0.221) total time=  38.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.078, test=-0.080) total time=  10.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.111, test=-0.113) total time=  21.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.203, test=-0.206) total time=  37.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.075, test=-0.076) total time=  11.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.112, test=-0.114) total time=  18.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.232, test=-0.236) total time=  38.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=  10.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.116, test=-0.118) total time=  18.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.220, test=-0.223) total time=  35.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.076, test=-0.077) total time=   9.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  10.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.117, test=-0.117) total time=  19.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.242, test=-0.244) total time=  36.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.081, test=-0.083) total time=   9.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.115, test=-0.117) total time=  18.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.161, test=-0.162) total time=  28.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.268, test=-0.273) total time=  58.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.281, test=-0.278) total time=  46.1s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.125, test=-0.128) total time=  22.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.208, test=-0.209) total time=  41.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.075, test=-0.076) total time=  12.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.133, test=-0.136) total time=  20.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.218, test=-0.222) total time=  43.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.077, test=-0.077) total time=  12.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.075) total time=  11.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.200, test=-0.197) total time=  29.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.281, test=-0.284) total time=  54.5s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.192, test=-0.197) total time=  31.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=60;, score=(train=-0.322, test=-0.319) total time=  54.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.182, test=-0.182) total time=  30.9s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.273, test=-0.276) total time=  59.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.215, test=-0.215) total time=  40.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.082, test=-0.084) total time=  12.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=  10.1s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=30;, score=(train=-0.209, test=-0.208) total time=  26.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.235, test=-0.236) total time=  40.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.104, test=-0.103) total time=  18.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.219, test=-0.218) total time=  35.0s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=  10.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.129, test=-0.130) total time=  19.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.250, test=-0.249) total time=  31.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.263, test=-0.263) total time=  49.7s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.243, test=-0.246) total time=  42.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.104, test=-0.104) total time=  19.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.209, test=-0.209) total time=  33.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.078, test=-0.078) total time=  10.5s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=20;, score=(train=-0.120, test=-0.120) total time=  18.2s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.249, test=-0.248) total time=  32.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.260, test=-0.260) total time=  51.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.265, test=-0.268) total time=  44.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.126, test=-0.126) total time=  19.3s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=18, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=40;, score=(train=-0.181, test=-0.180) total time=  32.3s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.072, test=-0.073) total time=  11.6s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  10.8s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.230, test=-0.231) total time=  26.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=2, n_estimators=50;, score=(train=-0.218, test=-0.218) total time=  49.4s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.113, test=-0.113) total time=  21.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=5, n_estimators=50;, score=(train=-0.299, test=-0.298) total time=  47.1s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.076, test=-0.076) total time=  13.0s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.186, test=-0.188) total time=  32.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=10, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.352, test=-0.350) total time=  56.3s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=2, n_estimators=40;, score=(train=-0.276, test=-0.274) total time=  40.6s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.080, test=-0.081) total time=  11.8s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=10;, score=(train=-0.075, test=-0.075) total time=  12.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=20;, score=(train=-0.111, test=-0.112) total time=  22.2s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.198, test=-0.196) total time=  37.9s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.079, test=-0.079) total time=  11.9s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.080, test=-0.080) total time=  12.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=30;, score=(train=-0.184, test=-0.187) total time=  34.4s\n",
      "[CV 1/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=20, base_estimator__min_samples_split=10, n_estimators=60;, score=(train=-0.334, test=-0.332) total time=  57.5s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=30;, score=(train=-0.164, test=-0.166) total time=  29.4s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=2, n_estimators=60;, score=(train=-0.277, test=-0.281) total time=  58.7s\n",
      "[CV 3/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=5, n_estimators=40;, score=(train=-0.216, test=-0.216) total time=  41.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=10;, score=(train=-0.085, test=-0.086) total time=  12.2s\n",
      "[CV 2/3] END base_estimator__max_leaf_nodes=20, base_estimator__min_samples_leaf=50, base_estimator__min_samples_split=10, n_estimators=20;, score=(train=-0.127, test=-0.130) total time=  24.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07278\n",
      " Mean squared error: 0.03475\n",
      "           R2 score: -0.15612\n",
      "CPU times: user 11.2 s, sys: 2.35 s, total: 13.6 s\n",
      "Wall time: 1h 32min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from Zillow.metrics import print_measure\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "dt = DecisionTreeRegressor()\n",
    "adaboost = AdaBoostRegressor(base_estimator=dt)\n",
    "\n",
    "params = {\n",
    "    'n_estimators': [x for x in range(10, 61, 10)],\n",
    "    'base_estimator__min_samples_split': [2, 5, 10],\n",
    "    'base_estimator__min_samples_leaf': [10, 20, 50],\n",
    "    'base_estimator__max_leaf_nodes': [x for x in range(2, 21, 2)]\n",
    "}\n",
    "\n",
    "gscv = GridSearchCV(estimator = adaboost, \n",
    "                    return_train_score=True,\n",
    "                    param_grid=params,\n",
    "                    cv = 3,\n",
    "                    scoring='neg_mean_absolute_error',\n",
    "                    verbose=4,\n",
    "                    n_jobs = -1)\n",
    "\n",
    "gscv.fit(X_train,y_train)\n",
    "\n",
    "print_measure(y_true=y_validation, y_pred=gscv.predict(X_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "38f220fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'base_estimator__max_leaf_nodes': 2,\n",
       " 'base_estimator__min_samples_leaf': 10,\n",
       " 'base_estimator__min_samples_split': 5,\n",
       " 'n_estimators': 10}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2ed76bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import Zillow.visualization as zv\n",
    "\n",
    "zv.plot_grid_search_validation_curve(gscv, 'n_estimators', ylim=(-.125,-.063))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "600b63a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zv.plot_grid_search_validation_curve(gscv, 'base_estimator__max_leaf_nodes', ylim=(-.09,-.068))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c8ccd7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zv.plot_grid_search_validation_curve(gscv, 'base_estimator__min_samples_leaf', ylim=(-.074,-.069))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c4115449",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zv.plot_grid_search_validation_curve(gscv, 'base_estimator__min_samples_split', ylim=(-.074,-.068))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d01f6f4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.07091\n",
      " Mean squared error: 0.03076\n",
      "           R2 score: -0.07510\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train, validation, test = zd.get_preprocessed_data()\n",
    "train = pd.concat([train, validation], axis=0, ignore_index=True)\n",
    "\n",
    "X_train, y_train = zd.split_x_y(train)\n",
    "X_test, y_test = zd.split_x_y(test)\n",
    "\n",
    "dt = DecisionTreeRegressor(max_leaf_nodes=2,\n",
    "                           min_samples_leaf=10,\n",
    "                           min_samples_split=5)\n",
    "\n",
    "adaboost = AdaBoostRegressor(base_estimator=dt, n_estimators=10)\n",
    "\n",
    "adaboost.fit(X_train,y_train)\n",
    "\n",
    "print_measure(y_true=y_test, y_pred=adaboost.predict(X_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}