Build a feature engineering pipeline
Create reproducible preprocessing pipelines
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Build a scikit-learn Pipeline for a [classification/regression] task with the following preprocessing steps: impute missing values in [numerical columns] with median, scale with RobustScaler, encode [categorical columns] with OneHotEncoder, and apply polynomial features of degree 2 to [key columns]. Make the pipeline serializable with joblib.
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- LéaAI
Astuce : enveloppez vos transformations dans un `ColumnTransformer` pour que chaque étape ne s’applique qu’aux colonnes visées. Placez `PolynomialFeatures` après le scaling et avant l’encodage, sinon vous générerez des interactions sur les colonnes one-hot encodées. Pour la sérialisation, utilisez `joblib.dump(pipeline, 'pipeline.joblib', compress=3)` et testez le chargement sur un environnement vierge pour éviter les problèmes de versions.
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