R: Tidy Characterizations of Model Performance

# Tidy Characterizations of Model Performance

## Documentation for package ‘yardstick’ version 0.0.8

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C D F G H I J K L M N P R S T V

## -- A --

accuracy | Accuracy
accuracy.data.frame | Accuracy
accuracy_vec | Accuracy
autoplot.conf_mat | Confusion Matrix for Categorical Data
autoplot.gain_df | Gain curve
autoplot.lift_df | Lift curve
autoplot.pr_df | Precision recall curve
autoplot.roc_df | Receiver operator curve
average_precision | Area under the precision recall curve
average_precision.data.frame | Area under the precision recall curve
average_precision_vec | Area under the precision recall curve

## -- B --

bal_accuracy | Balanced accuracy
bal_accuracy.data.frame | Balanced accuracy
bal_accuracy_vec | Balanced accuracy

## -- C --

ccc | Concordance correlation coefficient
ccc.data.frame | Concordance correlation coefficient
ccc_vec | Concordance correlation coefficient
classification_cost | Costs function for poor classification
classification_cost.data.frame | Costs function for poor classification
classification_cost_vec | Costs function for poor classification
conf_mat | Confusion Matrix for Categorical Data
conf_mat.data.frame | Confusion Matrix for Categorical Data
conf_mat.default | Confusion Matrix for Categorical Data
conf_mat.table | Confusion Matrix for Categorical Data

## -- D --

detection_prevalence | Detection prevalence
detection_prevalence.data.frame | Detection prevalence
detection_prevalence_vec | Detection prevalence
developer-helpers | Developer helpers
dots_to_estimate | Developer helpers

## -- F --

finalize_estimator | Developer helpers
finalize_estimator_internal | Developer helpers
f_meas | F Measure
f_meas.data.frame | F Measure
f_meas_vec | F Measure

## -- G --

gain_capture | Gain capture
gain_capture.data.frame | Gain capture
gain_capture_vec | Gain capture
gain_curve | Gain curve
gain_curve.data.frame | Gain curve
get_weights | Developer helpers

## -- H --

hpc_cv | Multiclass Probability Predictions
huber_loss | Huber loss
huber_loss.data.frame | Huber loss
huber_loss_pseudo | Psuedo-Huber Loss
huber_loss_pseudo.data.frame | Psuedo-Huber Loss
huber_loss_pseudo_vec | Psuedo-Huber Loss
huber_loss_vec | Huber loss

## -- I --

iic | Index of ideality of correlation
iic.data.frame | Index of ideality of correlation
iic_vec | Index of ideality of correlation

## -- J --

j_index | J-index
j_index.data.frame | J-index
j_index_vec | J-index

## -- K --

kap | Kappa
kap.data.frame | Kappa
kap_vec | Kappa

## -- L --

lift_curve | Lift curve
lift_curve.data.frame | Lift curve

## -- M --

mae | Mean absolute error
mae.data.frame | Mean absolute error
mae_vec | Mean absolute error
mape | Mean absolute percent error
mape.data.frame | Mean absolute percent error
mape_vec | Mean absolute percent error
mase | Mean absolute scaled error
mase.data.frame | Mean absolute scaled error
mase_vec | Mean absolute scaled error
mcc | Matthews correlation coefficient
mcc.data.frame | Matthews correlation coefficient
mcc_vec | Matthews correlation coefficient
metrics | General Function to Estimate Performance
metrics.data.frame | General Function to Estimate Performance
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
mn_log_loss | Mean log loss
mn_log_loss.data.frame | Mean log loss
mn_log_loss_vec | Mean log loss
mpe | Mean percentage error
mpe.data.frame | Mean percentage error
mpe_vec | Mean percentage error
msd | Mean signed deviation
msd.data.frame | Mean signed deviation
msd_vec | Mean signed deviation

## -- N --

new-metric | Construct a new metric function
new_class_metric | Construct a new metric function
new_numeric_metric | Construct a new metric function
new_prob_metric | Construct a new metric function
npv | Negative predictive value
npv.data.frame | Negative predictive value
npv_vec | Negative predictive value

## -- P --

pathology | Liver Pathology Data
ppv | Positive predictive value
ppv.data.frame | Positive predictive value
ppv_vec | Positive predictive value
precision | Precision
precision.data.frame | Precision
precision_vec | Precision
pr_auc | Area under the precision recall curve
pr_auc.data.frame | Area under the precision recall curve
pr_auc_vec | Area under the precision recall curve
pr_curve | Precision recall curve
pr_curve.data.frame | Precision recall curve

## -- R --

recall | Recall
recall.data.frame | Recall
recall_vec | Recall
rmse | Root mean squared error
rmse.data.frame | Root mean squared error
rmse_vec | Root mean squared error
roc_auc | Area under the receiver operator curve
roc_auc.data.frame | Area under the receiver operator curve
roc_auc_vec | 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_aunp.data.frame | Area under the ROC curve of each class against the rest, using the a priori class distribution
roc_aunp_vec | 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_aunu.data.frame | Area under the ROC curve of each class against the rest, using the uniform class distribution
roc_aunu_vec | Area under the ROC curve of each class against the rest, using the uniform class distribution
roc_curve | Receiver operator curve
roc_curve.data.frame | Receiver operator curve
rpd | Ratio of performance to deviation
rpd.data.frame | Ratio of performance to deviation
rpd_vec | Ratio of performance to deviation
rpiq | Ratio of performance to inter-quartile
rpiq.data.frame | Ratio of performance to inter-quartile
rpiq_vec | Ratio of performance to inter-quartile
rsq | R squared
rsq.data.frame | R squared
rsq_trad | R squared - traditional
rsq_trad.data.frame | R squared - traditional
rsq_trad_vec | R squared - traditional
rsq_vec | R squared

## -- S --

sens | Sensitivity
sens.data.frame | Sensitivity
sensitivity | Sensitivity
sensitivity_vec | Sensitivity
sens_vec | Sensitivity
smape | Symmetric mean absolute percentage error
smape.data.frame | Symmetric mean absolute percentage error
smape_vec | Symmetric mean absolute percentage error
solubility_test | Solubility Predictions from MARS Model
spec | Specificity
spec.data.frame | Specificity
specificity | Specificity
specificity_vec | Specificity
spec_vec | Specificity
summary.conf_mat | Summary Statistics for Confusion Matrices

## -- T --

tidy.conf_mat | Confusion Matrix for Categorical Data
two_class_example | Two Class Predictions

## -- V --

validate_estimator | Developer helpers