mlr3 (version 0.1.2)

MeasureClassifConfusion: Binary Classification Measures Derived from a Confusion Matrix

Description

All implemented Measures call confusion_measures() with the respective type internally.

Format

R6::R6Class() inheriting from MeasureClassif.

Construction

MeasureClassifConfusion$new(id = type, type)
mlr_measures("classif.tp")
mlr_measures("classif.fn")
mlr_measures("classif.fp")
mlr_measures("classif.tn")
mlr_measures("classif.tpr")
mlr_measures("classif.fnr")
mlr_measures("classif.fpr")
mlr_measures("classif.tnr")
mlr_measures("classif.ppv")
mlr_measures("classif.fdr")
mlr_measures("classif.for")
mlr_measures("classif.npv")
mlr_measures("classif.precision")
mlr_measures("classif.recall")
mlr_measures("classif.sensitivity")
mlr_measures("classif.specificity")
msr("classif.tp")
msr("classif.fn")
msr("classif.fp")
msr("classif.tn")
msr("classif.tpr")
msr("classif.fnr")
msr("classif.fpr")
msr("classif.tnr")
msr("classif.ppv")
msr("classif.fdr")
msr("classif.for")
msr("classif.npv")
msr("classif.precision")
msr("classif.recall")
msr("classif.sensitivity")
msr("classif.specificity")

• type :: character(1) See confusion_measures().

See Also

Dictionary of Measures: mlr_measures

Examples

# NOT RUN {
task = tsk("german_credit")
learner = lrn("classif.rpart")
p = learner$train(task)$predict(task)
measures = list(msr("classif.sensitivity"), msr("classif.specificity"))
round(p$score(measures), 2)
# }