Optimal Classification Cutoffs¶
Optimize classification thresholds to improve model performance. The library provides efficient algorithms for threshold selection in binary and multiclass classification.
Why Default 0.5 Thresholds Are Wrong:
Most classifiers output probabilities, but decisions need thresholds. The default τ = 0.5 assumes equal costs and balanced classes. Real problems have imbalanced data (fraud: 1%, disease: 5%) and asymmetric costs (missing fraud costs $1000, false alarm costs $1).
API 2.0.0 Features: