Tag: calibration

A scikit-learn NPS ordinal classifier with SMOTE, probability calibration, utility-based thresholding, and PSI drift checks. The parts that make it useful to the retention team, not just accurate on a dashboard.

How imbalanced classifiers can keep a strong AUC while producing probabilities that break thresholds, alerts, and cost-sensitive decisions in production.

How to add coverage-guaranteed prediction sets, temperature scaling calibration, and risk-coverage curves to a classifier using MAPIE — the pieces that make uncertainty quantification operationally useful rather than decorative.

A practical playbook for turning classifier scores into reliable probabilities that can support ranking, thresholds, SLAs, and cost-sensitive decisions.

A production-friendly scikit-learn pattern for mixed tabular data, class imbalance, calibrated probabilities, threshold selection, and model persistence.