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Calculate ROC Measures

Source: R/score_roc_measures.R
score_roc_measures.Rd

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("sonar"), ratio = 0.7)
task = tsk("sonar")
learner$train(task)
pred = learner$predict(task)
score_roc_measures(pred)
#> 
#> ── ROC Measures ──
#> 
#>     predicted
#> true M        R                                   
#>    M 95       10         tpr: 0.86      fnr: 0.14 
#>    R 16       87         fpr: 0.1       tnr: 0.9  
#>      ppv: 0.9 fomr: 0.16 lr_plus: 8.3   acc: 0.88 
#>      fdr: 0.1 npv: 0.84  lr_minus: 0.16 dor: 51.66
#> 
#> ── Abbreviations: 
#> • 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