score_roc_measures: Calculate ROC Measures

In mlr3: Machine Learning in R - Next Generation

Calculate ROC Measures

Description

Calculate a set of roc performance measures based on the confusion matrix.

• tpr True positive rate (Sensitivity, Recall)

• fpr False positive rate (Fall-out)

• fnr False negative rate (Miss rate)

• tnr True negative rate (Specificity)

• ppv Positive predictive value (Precision)

• fomr False omission rate

• lrp Positive likelihood ratio (LR+)

• fdr False discovery rate

• npv Negative predictive value

• acc Accuracy

• lrm Negative likelihood ratio (LR-)

• dor Diagnostic odds ratio

Usage

score_roc_measures(pred)

Arguments

pred	(PredictionClassif) The prediction object.

Value

list()
A list containing two elements confusion_matrix which is the 2 times 2 confusion matrix of absolute frequencies and measures, a list of the above mentioned measures.

Examples

learner = lrn("classif.rpart", predict_type = "prob")
splits = partition(task = tsk("pima"), ratio = 0.7)
task = tsk("pima")
learner$train(task)
pred = learner$predict(task)
score_roc_measures(pred)

mlr3 documentation built on Sept. 14, 2025, 1:08 a.m.