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… and XGBoost - Replace California Housing example with Bank Loan credit risk dataset loaded via kagglehub - Switch from RandomForestClassifier to XGBClassifier with enable_categorical support - Add per-feature evaluation loop (ROC AUC delta per engineered feature) - Add feature selection step (keep only features with positive delta) - Add save/load section demonstrating engineer persistence
- Add Dataset Statistics section to user_guide.rst documenting the y parameter in fit() and what stats are injected into the prompt - Add Saving and Reusing section to user_guide.rst with save()/load() - Add bin transformation to the supported transformations list - Add data leakage warning (fit on training data only) - Update all quickstart examples to include train/test split and pass y - Expand Feature Evaluation section in examples.rst to show per-feature selection pattern using mutual information scores - Add Saving and Loading section and Notebook Tutorial link to examples.rst
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Summary
examples/01_SKFeatureLLM_Tutorial.ipynb): replaced the California Housing example with a credit risk classification task using the Bank Loan dataset loaded viakagglehub. Switched fromRandomForestClassifiertoXGBClassifier, added a per-feature evaluation loop that measures ROC AUC delta for each engineered feature, a feature selection step (keep only features with positive delta), and asave()/load()section demonstrating engineer persistence.user_guide.rst: added a Dataset Statistics section documenting theyparameter infit()and the stats injected into the LLM prompt; added a Saving and Reusing section withsave()/load(); added thebintransformation to the supported-transformations list; added a data-leakage warning (fit on training data only).examples.rst: both classification and regression examples now include a train/test split and passytofit(); expanded the Feature Evaluation section to show per-feature filtering using mutual information scores; added Saving and Loading and Notebook Tutorial sections.get_started.rst: quickstart examples now include a train/test split, passy=y_traintofit(), and transform train and test independently; added a data-leakage callout.