Sklearn Pipelines

scikit-learn Pipelines

Overview

A Pipeline chains preprocessing and the estimator into one object so that every fit happens on training folds only. This makes leakage structurally impossible and makes the model trivially serializable for serving. If you remember one thing from this pack: wrap preprocessing in a Pipeline.

When to use

• Any sklearn model with preprocessing (scaling, encoding, imputing).
• You need cross-validation that includes preprocessing.
• You want one artifact to save and serve.

Canonical pattern

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import HistGradientBoostingClassifier

num = ["age", "income", "tenure"]
cat = ["country", "plan"]

preprocess = ColumnTransformer([
    ("num", Pipeline([
        ("impute", SimpleImputer(strategy="median")),
        ("scale", StandardScaler()),
    ]), num),
    ("cat", Pipeline([
        ("impute", SimpleImputer(strategy="most_frequent")),
        ("ohe", OneHotEncoder(handle_unknown="ignore")),
    ]), cat),
])

model = Pipeline([
    ("prep", preprocess),
    ("clf", HistGradientBoostingClassifier(random_state=42)),
])

model.fit(X_train, y_train)        # all preprocessing fit on train only
preds = model.predict(X_test)      # preprocessing reused, no leakage

Cross-validation the right way

from sklearn.model_selection import cross_val_score, StratifiedKFold

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
scores = cross_val_score(model, X, y, cv=cv, scoring="roc_auc")
# preprocessing is re-fit inside each fold automatically

Pair this with the hyperparameter-tuning skill — pass the whole pipeline to the search and tune with clf__ / prep__ prefixes.

Custom transformer

from sklearn.base import BaseEstimator, TransformerMixin

class LogTransform(BaseEstimator, TransformerMixin):
    def __init__(self, cols): self.cols = cols
    def fit(self, X, y=None): return self
    def transform(self, X):
        X = X.copy()
        X[self.cols] = np.log1p(X[self.cols])
        return X

Pitfalls

• scaler.fit_transform(X) before train_test_split — the #1 leakage bug. Fit inside the pipeline instead.
• OneHotEncoder without handle_unknown="ignore" crashes on unseen test categories.
• Imputing the target — pipelines transform X, never y; impute/clean targets separately and deliberately.
• Tuning preprocessing outside CV — keep it in the pipeline so search respects fold boundaries.

Hand-off

A single fitted Pipeline artifact that the model-evaluation, hyperparameter-tuning, and model-serving skills all consume directly (joblib.dump(model, "model.joblib")).