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scikit-learn patterns for single-feature LR and RF evaluation with cross-validation and AUC scoring.
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| name | sklearn-eval-patterns |
| description | scikit-learn patterns for single-feature LR and RF evaluation with cross-validation and AUC scoring. |
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold, cross_val_predict
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler
def single_feature_model(X, y, n_folds=5):
cv = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=42)
# LR (needs scaling)
X_scaled = StandardScaler().fit_transform(X)
lr_probs = cross_val_predict(
LogisticRegression(max_iter=1000, random_state=42),
X_scaled, y, cv=cv, method="predict_proba",
)[:, 1]
# RF (no scaling needed)
rf_probs = cross_val_predict(
RandomForestClassifier(
n_estimators=100, max_depth=5, min_samples_leaf=10,
class_weight="balanced", random_state=42,
),
X, y, cv=cv, method="predict_proba",
)[:, 1]
return {
"auc_lr": roc_auc_score(y, lr_probs),
"auc_rf": roc_auc_score(y, rf_probs),
}
class_weight="balanced" — groups are heavily imbalancedmax_depth=None for RF — will overfit on small strata