Man pages for yardstick
Tidy Characterizations of Model Performance

average_precisionArea under the precision recall curve
bal_accuracyBalanced accuracy
cccConcordance correlation coefficient
conf_matConfusion Matrix for Categorical Data
detection_prevalenceDetection prevalence
developer-helpersDeveloper helpers
f_measF Measure
gain_captureGain capture
gain_curveGain curve
hpc_cvMulticlass Probability Predictions
huber_lossHuber loss
huber_loss_pseudoPsuedo-Huber Loss
iicIndex of ideality of correlation
lift_curveLift curve
maeMean absolute error
mapeMean absolute percent error
maseMean absolute scaled error
mccMatthews correlation coefficient
metricsGeneral Function to Estimate Performance
metric_setCombine metric functions
metric_summarizerDeveloper function for summarizing new metrics
metric_vec_templateDeveloper function for calling new metrics
mn_log_lossMean log loss
mpeMean percentage error
new-metricConstruct a new metric function
npvNegative predictive value
pathologyLiver Pathology Data
ppvPositive predictive value
pr_aucArea under the precision recall curve
pr_curvePrecision recall curve
reexportsObjects exported from other packages
rmseRoot mean squared error
roc_aucArea under the receiver operator curve
roc_aunpArea under the ROC curve of each class against the rest,...
roc_aunuArea under the ROC curve of each class against the rest,...
roc_curveReceiver operator curve
rpdRatio of performance to deviation
rpiqRatio of performance to inter-quartile
rsqR squared
rsq_tradR squared - traditional
smapeSymmetric mean absolute percentage error
solubility_testSolubility Predictions from MARS Model
summary.conf_matSummary Statistics for Confusion Matrices
two_class_exampleTwo Class Predictions
yardstick documentation built on July 13, 2020, 5:09 p.m.