Machine Learning
Ds Evaluation
Use when evaluating model performance or reporting results. Triggers on: accuracy, precision, recall, F1, AUC, ROC, confusion matrix, classification report, log loss, Brier score, calibration curve, SHAP, feature importance, MAE, RMSE, R2, mean absolute error, subgroup, fairness, baseline comparison, predict_proba, score, evaluate, metrics, test set, held-out, explainability, shap.Explainer, shap_values, CalibratedClassifierCV, cross_val_predict.