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scikit-learn Workflow

Split, fit, evaluate — the classic ML loop.

python
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, stratify=y)
clf = make_pipeline(StandardScaler(), LogisticRegression())
clf.fit(X_tr, y_tr)
print(classification_report(y_te, clf.predict(X_te)))
Tip: Fit scalers on training data only — pipelines prevent data leakage.

⚡ Quick check

  • ✓ fit / predict / score
  • ✓ Pipelines prevent leakage
  • ✓ stratify=y for imbalanced data