ROC and Precision-Recall Explorer

Model Evaluation

ROC and Precision-Recall Explorer

Thresholds, class imbalance, and classification trade-offs.

What This Shows

Explore how ROC and Precision-Recall curves respond when prevalence, class separation, and decision thresholds change. The demo makes visible a common production ML risk: a model can look acceptable under one metric while failing the operating point that matters for the positive class.

ROC Precision-Recall Class imbalance Thresholds Model evaluation

Technical Challenge

Choosing metrics and thresholds when false positives and false negatives have different operational costs.

My Contribution

Built an interactive simulator that links score distributions, confusion-matrix behavior, and evaluation curves.

Industry Transfer

Fraud detection, medical triage, rare-event monitoring, ranking, recommender evaluation.

Connected Work

TOROS rare-event detection and production model evaluation.