accuracy                Accuracy
average_precision       Area under the precision recall curve
bal_accuracy            Balanced accuracy
ccc                     Concordance correlation coefficient
classification_cost     Costs function for poor classification
conf_mat                Confusion Matrix for Categorical Data
detection_prevalence    Detection prevalence
f_meas                  F Measure
gain_capture            Gain capture
gain_curve              Gain curve
get_weights             Developer helpers
hpc_cv                  Multiclass Probability Predictions
huber_loss              Huber loss
huber_loss_pseudo       Psuedo-Huber Loss
iic                     Index of ideality of correlation
j_index                 J-index
kap                     Kappa
lift_curve              Lift curve
mae                     Mean absolute error
mape                    Mean absolute percent error
mase                    Mean absolute scaled error
mcc                     Matthews correlation coefficient
metric_set              Combine metric functions
metric_summarizer       Developer function for summarizing new metrics
metric_tweak            Tweak a metric function
metric_vec_template     Developer function for calling new metrics
metrics                 General Function to Estimate Performance
mn_log_loss             Mean log loss for multinomial data
mpe                     Mean percentage error
msd                     Mean signed deviation
new-metric              Construct a new metric function
npv                     Negative predictive value
pathology               Liver Pathology Data
poisson_log_loss        Mean log loss for Poisson data
ppv                     Positive predictive value
pr_auc                  Area under the precision recall curve
pr_curve                Precision recall curve
precision               Precision
recall                  Recall
rmse                    Root mean squared error
roc_auc                 Area under the receiver operator curve
roc_aunp                Area under the ROC curve of each class against
                        the rest, using the a priori class distribution
roc_aunu                Area under the ROC curve of each class against
                        the rest, using the uniform class distribution
roc_curve               Receiver operator curve
rpd                     Ratio of performance to deviation
rpiq                    Ratio of performance to inter-quartile
rsq                     R squared
rsq_trad                R squared - traditional
sens                    Sensitivity
smape                   Symmetric mean absolute percentage error
solubility_test         Solubility Predictions from MARS Model
spec                    Specificity
summary.conf_mat        Summary Statistics for Confusion Matrices
two_class_example       Two Class Predictions
