Building Your First Pipeline: Scaler Plus Classifier
Learners will chain StandardScaler and LogisticRegression into a Pipeline, call fit and predict, and confirm the scaler was fitted only on training data.
What Is a scikit-learn Pipeline?
A Pipeline chains multiple processing steps into a single estimator object. Each step except the last must implement fit and transform; the final step only needs fit and predict. Calling pipeline.fit(X, y) runs all steps in sequence, and pipeline.predict(X) passes data through all transforms before classifying. This eliminates bookkeeping errors and prevents data leakage.
Why Pipelines Prevent Leakage
If you fit a StandardScaler on the full dataset and then split into train/test, the scaler has seen test-set statistics — this is data leakage. A Pipeline solves this automatically: when you pass a Pipeline to cross_val_score or GridSearchCV, the entire pipeline (including the scaler) is re-fitted from scratch on each training fold, so the test fold never influences the scaler parameters.
All lessons in this course
- Building Your First Pipeline: Scaler Plus Classifier
- ColumnTransformer Inside a Pipeline
- Cross-Validating and Grid-Searching a Full Pipeline
- Saving and Loading a Pipeline with joblib