class-metrics: Class metrics

In yardstick: Tidy Characterizations of Model Performance

Class metrics

Description

Class metrics evaluate hard classification predictions where both truth and estimate are factors. These metrics compare predicted classes directly against the true classes.

Input requirements

• truth: factor

• estimate: factor

Available metrics

accuracy()

Direction: maximize. Range: [0, 1]

bal_accuracy()

Direction: maximize. Range: [0, 1]

detection_prevalence()

Direction: maximize. Range: [0, 1]

f_meas()

Direction: maximize. Range: [0, 1]

fall_out()

Direction: minimize. Range: [0, 1]

j_index()

Direction: maximize. Range: [-1, 1]

kap()

Direction: maximize. Range: [-1, 1]

markedness()

Direction: maximize. Range: [-1, 1]

mcc()

Direction: maximize. Range: [-1, 1]

miss_rate()

Direction: minimize. Range: [0, 1]

npv()

Direction: maximize. Range: [0, 1]

ppv()

Direction: maximize. Range: [0, 1]

precision()

Direction: maximize. Range: [0, 1]

recall()

Direction: maximize. Range: [0, 1]

roc_dist()

Direction: minimize. Range: [0, 1.4142135623731]

sedi()

Direction: maximize. Range: [-1, 1]

sens()

Direction: maximize. Range: [0, 1]

sensitivity()

Direction: maximize. Range: [0, 1]

spec()

Direction: maximize. Range: [0, 1]

specificity()

Direction: maximize. Range: [0, 1]

See Also

prob-metrics for class probability metrics

ordered-prob-metrics for ordered probability metrics

vignette("metric-types") for an overview of all metric types

Examples

data("two_class_example")

head(two_class_example)

accuracy(two_class_example, truth, predicted)

yardstick documentation built on April 8, 2026, 1:06 a.m.