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