model-evaluationlisted

# Model Evaluation Patterns ## Overview Comprehensive model evaluation using yardstick and related packages. Covers metrics for classification, regression, and survival outcomes, plus calibration and uncertainty quantification. ## Classification Metrics ### Binary Classification ```r library(yardstick) # Hard predictions (class) predictions |> accuracy(truth = outcome, estimate = .pred_class) predictions |> sens(truth = outcome, estimate = .pred_class) # sensitivity/recall predictions |> spec(truth = outcome, estimate = .pred_class) # specificity predictions |> ppv(truth = outcome, estimate = .pred_class) # precision predictions |> npv(truth = outcome, estimate = .pred_class) predictions |> f_meas(truth = outcome, estimate = .pred_class) # F1 score predictions |> kap(truth = outcome, estimate = .pred_class) # Cohen's kappa predictions |> mcc(truth = outcome, estimate = .pred_class) # Matthews correlation ``` ### Probability-Based Metrics ```r # ROC AUC predictions |> roc_auc(truth = outcome, .pred_positive_class) # PR AUC (better for imbalanced data) predictions |> pr_auc(truth = outcome, .pred_positive_class) # Brier score predictions |> brier_class(truth = outcome, .pred_positive_class) # Log loss predictions |> mn_log_loss(truth = outcome, .pred_positive_class) # Gain capture (lift) predictions |> gain_capture(truth = outcome, .pred_positive_class) ``` ### Multi-Class Classification ```r # Macro-averaged (average across