Build the preprocessing pipeline as the feature artifact
Assemble a feature preprocessing pipeline that preserves train-test boundaries and serving consistency.
Create model-ready features from raw lead rows with numeric, categorical, and short text fields. Pipeline artifact: column-specific transforms fit on train, reused unchanged for validation and serving Manual notebook preprocessing can leak validation data, drop unseen categories, or create different columns each run. Define Name the behavior, value, entity, and decision this feature is meant to represent. A clear definition prevents a convenient column from masquerading as business signal. Bound Set the prediction cutoff, training-only fit boundary, rare threshold, or freshness rule before computing the feature. The boundary is what keeps validation honest and serving reproducible. Transform Apply the formula,…
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