scikit-learn Pipeline

Chain preprocessing and modeling steps into one clean, leak-proof object

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Explanation

A Pipeline chains preprocessing steps and the final estimator into a single object. This prevents data leakage and makes code cleaner.

Without pipeline (leaky):

python scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Easy to forget this step! model.fit(X_train_scaled, y_train)

With pipeline (correct):

python from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression pipe = Pipeline([ ('scaler', StandardScaler()), ('model', LogisticRegression()) ]) pipe.fit(X_train, y_train) # scales then trains pipe.score(X_test, y_test) # scales then evaluates (no leakage) pipe.predict(X_new) # scales then predicts

Why pipelines prevent leakage: fit_transform() on the full dataset before splitting leaks test statistics into training. Pipelines call fit_transform on train only, transform on test — automatically.

Works with cross-validation:

python from sklearn.model_selection import cross_val_score scores = cross_val_score(pipe, X, y, cv=5)

Examples

Full pipeline with preprocessing

The pipeline handles all transformations internally

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf',    LogisticRegression(max_iter=200))
])

pipe.fit(X_train, y_train)
print(f"Accuracy: {pipe.score(X_test, y_test):.3f}")

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