Resampling and Class Weights

#Class weights per library

balanced weights each class by n / (n_classes * n_class). Square-root weighting is a milder compromise.

#imbalanced-learn samplers

from imblearn.pipeline import Pipeline
from imblearn.under_sampling import RandomUnderSampler, TomekLinks, EditedNearestNeighbours, NearMiss
from imblearn.over_sampling import RandomOverSampler, SMOTE, BorderlineSMOTE, ADASYN, SMOTENC, SMOTEN
from imblearn.combine import SMOTETomek, SMOTEENN

pipe = Pipeline([("prep", prep), ("sampler", SMOTE(k_neighbors=5, random_state=0)), ("model", clf)])

• sampling_strategy as a float = desired minority/majority ratio after resampling; it must be larger than the current ratio for undersampling, or it raises.
• SMOTENC handles mixed numeric + categorical features (categorical_features=[...]); SMOTEN all-categorical.
• SMOTE needs numeric, scaled features (it interpolates); put it after the preprocessing step.
• Samplers in an imblearn Pipeline run only during fit: validation folds are never resampled.

#When resampling helps (and when it does not)

Always compare on identical folds with the decision metric.

#Experiment template

from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=0)
variants = {
    "none": Pipeline([("prep", prep), ("model", base())]),
    "weights": Pipeline([("prep", prep), ("model", base(class_weight="balanced"))]),
    "under_0.2": Pipeline([("prep", prep), ("s", RandomUnderSampler(sampling_strategy=0.2, random_state=0)), ("model", base())]),
    "smote": Pipeline([("prep", prep), ("s", SMOTE(random_state=0)), ("model", base())]),
}
for name, p in variants.items():
    s = cross_val_score(p, X, y, cv=cv, scoring="average_precision")
    print(f"{name:<10} {s.mean():.4f} ± {s.std():.4f}")

#Leakage reminder

Resampling before the train/test split (or before CV) puts synthetic neighbours of test rows into training and inflates scores. target-leakage-detector's code_leak_lint.py flags it (LK002).

#Undersampling for speed

With millions of negatives, keep all positives and a random 5-20% of negatives: training is much faster and ranking quality barely changes. Record the sampling rate and prior-correct probabilities afterwards.