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Logistic regression solution notebook refactoring #645
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carlotta94c
commented
Mar 7, 2023
- Fixing features encoding (using ordinal encoder for Item size, categorical encoder for all the other features and label encoder for Color) as recommended by scikit-learn documentation
- Using F1 score instead of accuracy to evaluate the model since the dataset is highly skewed
- Updating plots to better show the data before and after encofings and the relationships between label and features
- Fixing ROC curve plot using matplotlib
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Looks great! |
Should we remove the output from this notebook? Would make it smaller and easier to diff |
@jimbobbennett |
I was thinking in general, just this notebook was in the PR so made me think of it. Is it intentional to have the output in the notebooks then? |
Yes! :) |
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Logistic regression solution notebook refactoring