Pipeline Creation
Pipelines chain preprocessing and modeling steps into a single object.
Pipeline Structure
Pipeline([('scaler', StandardScaler()), ('model', LogisticRegression())]). Fit the entire pipeline: pipeline.fit(X, y).
Predict with pipeline: pipeline.predict(X_new). Score: pipeline.score(X_test, y_test).
Making Predictions
The pipeline applies all steps in sequence. Preprocessing happens before modeling. This ensures consistent treatment.
Using pipelines avoids data leakage. Transformations use only training data during fit.
Key Takeaways
- Pipelines ensure consistent preprocessing
- They prevent data leakage
- Simplify deployment with single object