rigorous-evaluation

Use when evaluating or reporting model performance and choosing metrics, thresholds, and plots that FIT the problem type. Picks the right metrics per task (binary uses ROC-AUC and PR-AUC; multiclass uses macro F1 and a confusion matrix; detection uses mAP; segmentation uses Dice and IoU; regression uses MAE, RMSE, R2; ranking uses nDCG) and does not force AUC or ROC where they do not apply. Also covers calibration, thresholds chosen on validation, confidence intervals, and honest reporting. Triggers on 'evaluate', 'metrics', 'F1', 'AUC', 'ROC', 'mAP', 'RMSE', 'confusion matrix', 'which threshold', 'is this result good'.