Options: oversampling minority class (SMOTE), undersampling majority, class_weight='balanced' in sklearn, adjusting decision threshold post-training, or using F1/AUC-ROC instead of accuracy.

Options: oversampling minority class (SMOTE), undersampling majority, class_weight='balanced' in sklearn, adjusting decision threshold post-training, or using F1/AUC-ROC instead of accuracy. In resume screening, threshold tuning + weighted scoring worked better than resampling since the minority examples were genuine top candidates, not noise.