Function reference • yardstick

Classification Metrics

sens() sens_vec() sensitivity() sensitivity_vec(): Sensitivity

spec() spec_vec() specificity() 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 for multinomial data

classification_cost() classification_cost_vec(): Costs function for poor classification

Regression Metrics

rmse() rmse_vec(): Root mean squared error

rsq() rsq_vec(): R squared

rsq_trad() rsq_trad_vec(): R squared - traditional

msd() msd_vec(): Mean signed deviation

mae() mae_vec(): Mean absolute error

mpe() mpe_vec(): Mean percentage 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

poisson_log_loss() poisson_log_loss_vec(): Mean log loss for Poisson data

Curve Functions

roc_curve(): Receiver operator curve

pr_curve(): Precision recall curve

gain_curve(): Gain curve

lift_curve(): Lift curve

Other Functions

metrics(): General Function to Estimate Performance

metric_set(): Combine metric functions

metric_tweak(): Tweak a metric function

conf_mat() tidy(<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

new_class_metric() new_prob_metric() new_numeric_metric(): Construct a new metric function

Data Sets

hpc_cv: Multiclass Probability Predictions

pathology: Liver Pathology Data

solubility_test: Solubility Predictions from MARS Model

two_class_example: Two Class Predictions