- Summary
4-6 sentences summarizing general approach used to build the model, including
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- Features Selection / Extraction
Describe how features were generated or selected from the training data. Provide a list and brief description of any key new or selected features.
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- Modeling Techniques and Training
Details of the model and training procedures for each technique used in the final model. If models were combined or ensembled, describe that procedure as well. If external data was used, explain how this data was obtained and used.
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- Code Description
Provide a description of your code here. Code itself should be commented clearly and concisely. For each function provide
function inputs function outputs what function does
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- Dependencies
List of all dependencies, libraries, functions, packages or other third-party code used to generate your solution.
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- How To Generate the Solution (aka README file)
Provide step-by-step instructions for how to create the solutions file from the code provided. Include that description here and in a separate README file to accompany the code.
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- Additional Comments and Observations
Any additional comments or observations you have about the data set, model, or model development process. Discuss other approaches that were attempted but not used.
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- Simple Features and Methods
Was there a small subset of features and a single supervised machine learning method that got you 90-95% of the way to your final performance? If so, please describe and document this here.
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- Figures
Please include any figures or visualizations that you made of the data or the training process that you found useful or interesting.
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- References
Citations to references, websites, blog posts, and external sources of information where appropriate.
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