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danhalligan committed Aug 22, 2024
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4 changes: 2 additions & 2 deletions 03-linear-regression.md
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6 changes: 3 additions & 3 deletions 04-classification.md
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Expand Up @@ -679,16 +679,16 @@ fit <- knn(
```
##
## fit Down Up
## Down 21 29
## Up 22 32
## Down 21 30
## Up 22 31
```

``` r
sum(diag(t)) / sum(t)
```

```
## [1] 0.5096154
## [1] 0.5
```

> h. Repeat (d) using naive Bayes.
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2 changes: 1 addition & 1 deletion 05-resampling-methods.md
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Expand Up @@ -170,7 +170,7 @@ mean(store)
```
```
## [1] 0.6424
## [1] 0.6308
```

The probability of including $4$ when resampling numbers $1...100$ is close to
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2 changes: 1 addition & 1 deletion 08-tree-based-methods.md
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Expand Up @@ -1150,7 +1150,7 @@ bart <- gbart(College[train, pred], College[train, "Outstate"],
## done 800 (out of 1100)
## done 900 (out of 1100)
## done 1000 (out of 1100)
## time: 3s
## time: 4s
## trcnt,tecnt: 1000,1000
```

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66 changes: 33 additions & 33 deletions 10-deep-learning.md
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Expand Up @@ -393,15 +393,15 @@ npred <- predict(nn, x[testid, ])
```

```
## 6/6 - 0s - 54ms/epoch - 9ms/step
## 6/6 - 0s - 61ms/epoch - 10ms/step
```

``` r
mean(abs(y[testid] - npred))
```

```
## [1] 2.334041
## [1] 2.219039
```

In this case, the neural network outperforms logistic regression having a lower
Expand Down Expand Up @@ -433,7 +433,7 @@ model <- application_resnet50(weights = "imagenet")

```
## Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
## 8192/102967424 [..............................] - ETA: 0s 8085504/102967424 [=>............................] - ETA: 0s 21987328/102967424 [=====>........................] - ETA: 0s 36618240/102967424 [=========>....................] - ETA: 0s 51453952/102967424 [=============>................] - ETA: 0s 66551808/102967424 [==================>...........] - ETA: 0s 80912384/102967424 [======================>.......] - ETA: 0s 95641600/102967424 [==========================>...] - ETA: 0s102967424/102967424 [==============================] - 0s 0us/step
## 8192/102967424 [..............................] - ETA: 0s 3956736/102967424 [>.............................] - ETA: 1s 4202496/102967424 [>.............................] - ETA: 2s 8396800/102967424 [=>............................] - ETA: 1s 16785408/102967424 [===>..........................] - ETA: 1s 25174016/102967424 [======>.......................] - ETA: 1s 33562624/102967424 [========>.....................] - ETA: 0s 41951232/102967424 [===========>..................] - ETA: 0s 50905088/102967424 [=============>................] - ETA: 0s 58728448/102967424 [================>.............] - ETA: 0s 67117056/102967424 [==================>...........] - ETA: 0s 83894272/102967424 [=======================>......] - ETA: 0s101908480/102967424 [============================>.] - ETA: 0s102967424/102967424 [==============================] - 1s 0us/step
```

``` r
Expand Down Expand Up @@ -729,7 +729,7 @@ kpred <- predict(model, xrnn[!istrain,, ])
```

```
## [1] 0.4133125
## [1] 0.412886
```

Both models estimate the same number of coefficients/weights (16):
Expand Down Expand Up @@ -762,25 +762,25 @@ model$get_weights()

```
## [[1]]
## [,1]
## [1,] -0.03262059
## [2,] 0.09806149
## [3,] 0.19123746
## [4,] -0.00672294
## [5,] 0.11956818
## [6,] -0.08616812
## [7,] 0.03884261
## [8,] 0.07576967
## [9,] 0.16982540
## [10,] -0.02789208
## [11,] 0.02615459
## [12,] -0.76362336
## [13,] 0.09488130
## [14,] 0.51370680
## [15,] 0.48065400
## [,1]
## [1,] -0.031145222
## [2,] 0.101065643
## [3,] 0.141815767
## [4,] -0.004181504
## [5,] 0.116010934
## [6,] -0.003764492
## [7,] 0.038601257
## [8,] 0.078083567
## [9,] 0.137415737
## [10,] -0.029184511
## [11,] 0.036070298
## [12,] -0.821708620
## [13,] 0.095548652
## [14,] 0.511229098
## [15,] 0.521453559
##
## [[2]]
## [1] -0.005785846
## [1] -0.006889343
```

The flattened RNN has a lower $R^2$ on the test data than our `lm` model
Expand Down Expand Up @@ -833,11 +833,11 @@ xfun::cache_rds({
```

```
## 56/56 - 0s - 64ms/epoch - 1ms/step
## 56/56 - 0s - 66ms/epoch - 1ms/step
```

```
## [1] 0.4267343
## [1] 0.4271516
```

This approach improves our $R^2$ over the linear model above.
Expand Down Expand Up @@ -906,11 +906,11 @@ xfun::cache_rds({
```

```
## 56/56 - 0s - 136ms/epoch - 2ms/step
## 56/56 - 0s - 133ms/epoch - 2ms/step
```

```
## [1] 0.4447892
## [1] 0.4405331
```

### Question 13
Expand Down Expand Up @@ -966,21 +966,21 @@ xfun::cache_rds({

```
## Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
## 8192/17464789 [..............................] - ETA: 0s 7127040/17464789 [===========>..................] - ETA: 0s 8396800/17464789 [=============>................] - ETA: 0s17464789/17464789 [==============================] - 0s 0us/step
## 8192/17464789 [..............................] - ETA: 0s 3784704/17464789 [=====>........................] - ETA: 0s 4202496/17464789 [======>.......................] - ETA: 0s 8396800/17464789 [=============>................] - ETA: 0s17464789/17464789 [==============================] - 0s 0us/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
```



| Max Features| Accuracy|
|------------:|--------:|
| 1000| 0.84516|
| 3000| 0.87840|
| 5000| 0.86400|
| 10000| 0.87200|
| 1000| 0.86084|
| 3000| 0.87224|
| 5000| 0.87460|
| 10000| 0.86180|

Varying the dictionary size does not make a substantial impact on our estimates
of accuracy. However, the models do take a substantial amount of time to fit and
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6 changes: 3 additions & 3 deletions classification.html
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Expand Up @@ -1016,10 +1016,10 @@ <h3><span class="header-section-number">4.2.1</span> Question 13<a href="classif
<span id="cb199-6"><a href="classification.html#cb199-6" aria-hidden="true"></a>(t &lt;-<span class="st"> </span><span class="kw">table</span>(fit, Weekly[<span class="op">!</span>train, ]<span class="op">$</span>Direction))</span></code></pre></div>
<pre><code>##
## fit Down Up
## Down 21 29
## Up 22 32</code></pre>
## Down 21 30
## Up 22 31</code></pre>
<div class="sourceCode" id="cb201"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb201-1"><a href="classification.html#cb201-1" aria-hidden="true"></a><span class="kw">sum</span>(<span class="kw">diag</span>(t)) <span class="op">/</span><span class="st"> </span><span class="kw">sum</span>(t)</span></code></pre></div>
<pre><code>## [1] 0.5096154</code></pre>
<pre><code>## [1] 0.5</code></pre>
<blockquote>
<ol start="8" style="list-style-type: lower-alpha">
<li>Repeat (d) using naive Bayes.</li>
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