Tidy Characterizations of Model Performance

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Documentation for package ‘yardstick’ version 1.2.0

• 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 Y

-- A --

accuracy	Accuracy
accuracy.data.frame	Accuracy
accuracy_vec	Accuracy
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
brier_class	Brier score for classification models
brier_class.data.frame	Brier score for classification models
brier_class_vec	Brier score for classification models

-- C --

ccc	Concordance correlation coefficient
ccc.data.frame	Concordance correlation coefficient
ccc_vec	Concordance correlation coefficient
check_class_metric	Developer function for checking inputs in new metrics
check_dynamic_survival_metric	Developer function for checking inputs in new metrics
check_metric	Developer function for checking inputs in new metrics
check_numeric_metric	Developer function for checking inputs in new metrics
check_prob_metric	Developer function for checking inputs in new metrics
check_static_survival_metric	Developer function for checking inputs in new metrics
classification_cost	Costs function for poor classification
classification_cost.data.frame	Costs function for poor classification
classification_cost_vec	Costs function for poor classification
class_metric_summarizer	Developer function for summarizing new metrics
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
curve_metric_summarizer	Developer function for summarizing new metrics
curve_survival_metric_summarizer	Developer function for summarizing new metrics

-- 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
dynamic_survival_metric_summarizer	Developer function for summarizing new metrics

-- 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
lung_surv	Survival Analysis Results

-- 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
metric-summarizers	Developer function for summarizing new metrics
metrics	General Function to Estimate Performance
metrics.data.frame	General Function to Estimate Performance
metric_set	Combine metric functions
metric_tweak	Tweak a metric function
mn_log_loss	Mean log loss for multinomial data
mn_log_loss.data.frame	Mean log loss for multinomial data
mn_log_loss_vec	Mean log loss for multinomial data
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_dynamic_survival_metric	Construct a new metric function
new_integrated_survival_metric	Construct a new metric function
new_numeric_metric	Construct a new metric function
new_prob_metric	Construct a new metric function
new_static_survival_metric	Construct a new metric function
npv	Negative predictive value
npv.data.frame	Negative predictive value
npv_vec	Negative predictive value
numeric_metric_summarizer	Developer function for summarizing new metrics

-- P --

pathology	Liver Pathology Data
poisson_log_loss	Mean log loss for Poisson data
poisson_log_loss.data.frame	Mean log loss for Poisson data
poisson_log_loss_vec	Mean log loss for Poisson 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
prob_metric_summarizer	Developer function for summarizing new metrics
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.data.frame	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.data.frame	Specificity
specificity_vec	Specificity
spec_vec	Specificity
static_survival_metric_summarizer	Developer function for summarizing new metrics
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

-- Y --

yardstick_any_missing	Developer function for handling missing values in new metrics
yardstick_remove_missing	Developer function for handling missing values in new metrics