Function reference • yardstick

Classification Metrics
`sens()` `sensitivity()` `sens_vec()` `sensitivity_vec()`	Sensitivity
`spec()` `specificity()` `spec_vec()` `specificity_vec()`	Specificity
`recall()` `recall_vec()`	Recall
`precision()` `precision_vec()`	Precision
`mcc()` `mcc_vec()`	Matthews correlation coefficient
`j_index()` `j_index_vec()`	J-index
`f_meas()` `f_meas_vec()`	F Measure
`accuracy()` `accuracy_vec()`	Accuracy
`kap()` `kap_vec()`	Kappa
`ppv()` `ppv_vec()`	Positive predictive value
`npv()` `npv_vec()`	Negative predictive value
`bal_accuracy()` `bal_accuracy_vec()`	Balanced accuracy
`detection_prevalence()` `detection_prevalence_vec()`	Detection prevalence
Class Probability Metrics
`roc_auc()` `roc_auc_vec()`	Area under the receiver operator curve
`roc_aunp()` `roc_aunp_vec()`	Area under the ROC curve of each class against the rest, using the a priori class distribution
`roc_aunu()` `roc_aunu_vec()`	Area under the ROC curve of each class against the rest, using the uniform class distribution
`pr_auc()` `pr_auc_vec()`	Area under the precision recall curve
`average_precision()` `average_precision_vec()`	Area under the precision recall curve
`gain_capture()` `gain_capture_vec()`	Gain capture
`mn_log_loss()` `mn_log_loss_vec()`	Mean log loss
Regression Metrics
`rmse()` `rmse_vec()`	Root mean squared error
`rsq()` `rsq_vec()`	R squared
`rsq_trad()` `rsq_trad_vec()`	R squared - traditional
`mae()` `mae_vec()`	Mean absolute error
`mape()` `mape_vec()`	Mean absolute percent error
`smape()` `smape_vec()`	Symmetric mean absolute percentage error
`mase()` `mase_vec()`	Mean absolute scaled error
`ccc()` `ccc_vec()`	Concordance correlation coefficient
`rpiq()` `rpiq_vec()`	Ratio of performance to inter-quartile
`rpd()` `rpd_vec()`	Ratio of performance to deviation
`huber_loss()` `huber_loss_vec()`	Huber loss
`huber_loss_pseudo()` `huber_loss_pseudo_vec()`	Psuedo-Huber Loss
`iic()` `iic_vec()`	Index of ideality of correlation
Curve Functions
`roc_curve()` `autoplot.roc_df()`	Receiver operator curve
`pr_curve()` `autoplot.pr_df()`	Precision recall curve
`gain_curve()` `autoplot.gain_df()`	Gain curve
`lift_curve()` `autoplot.lift_df()`	Lift curve
Other Functions
`metrics()`	General Function to Estimate Performance
`metric_set()`	Combine metric functions
`conf_mat()` `tidy(<conf_mat>)` `autoplot.conf_mat()`	Confusion Matrix for Categorical Data
`summary(<conf_mat>)`	Summary Statistics for Confusion Matrices
Development Functions
`metric_summarizer()`	Developer function for summarizing new metrics
`metric_vec_template()`	Developer function for calling new metrics
`get_weights()` `finalize_estimator()` `finalize_estimator_internal()` `dots_to_estimate()` `validate_estimator()`	Developer helpers
Data Sets
`hpc_cv`	Multiclass Probability Predictions
`pathology`	Liver Pathology Data
`solubility_test`	Solubility Predictions from MARS Model
`two_class_example`	Two Class Predictions

Reference

Classification Metrics

Class Probability Metrics

Regression Metrics

Curve Functions

Other Functions

Development Functions

Data Sets