List all available performance metrics.
Value
A data frame with the following columns:
metric- the optimization or tuning metric;description- a brief description about the metric;task- whether the metric is suitable for regression or classification;smaller_is_better- logical indicating whether or not a smaller value of the metric is considered better.yardstick_function- the name of the corresponding vector function from the yardstick package.
Examples
(metrics <- list_metrics())
#> metric description
#> 1 accuracy Classification accuracy
#> 2 bal_accuracy Balanced classification accuracy
#> 3 youden Youden's index (or Youden's J statistic)
#> 4 roc_auc Area under ROC curve
#> 5 pr_auc Area under precision-recall (PR) curve
#> 6 logloss Log loss
#> 7 brier Brier score
#> 8 mae Mean absolute error
#> 9 mape Mean absolute percentage error
#> 10 rmse Root mean squared error
#> 11 rsq R-squared (correlation)
#> 12 rsq_trad R-squared (traditional)
#> task smaller_is_better yardstick_function
#> 1 Binary/multiclass classification FALSE accuracy_vec
#> 2 Binary/multiclass classification FALSE bal_accuracy_vec
#> 3 Binary/multiclass classification FALSE j_index_vec
#> 4 Binary classification FALSE roc_auc_vec
#> 5 Binary classification FALSE pr_auc_vec
#> 6 Binary/multiclass classification TRUE mn_log_loss_vec
#> 7 Binary/multiclass classification TRUE brier_class_vec
#> 8 Regression TRUE mae_vec
#> 9 Regression TRUE mape_vec
#> 10 Regression TRUE rmse_vec
#> 11 Regression FALSE rsq_vec
#> 12 Regression FALSE rsq_trad_vec
metrics[metrics$task == "Binary classification", ]
#> metric description task
#> 4 roc_auc Area under ROC curve Binary classification
#> 5 pr_auc Area under precision-recall (PR) curve Binary classification
#> smaller_is_better yardstick_function
#> 4 FALSE roc_auc_vec
#> 5 FALSE pr_auc_vec