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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("pima"), ratio = 0.7)
task = tsk("pima")
learner$train(task)
pred = learner$predict(task)
score_roc_measures(pred)
#>      predicted
#> true  pos       neg                                 
#>   pos 207       73         tpr: 0.77      fnr: 0.23 
#>   neg 61        427        fpr: 0.15      tnr: 0.85 
#>       ppv: 0.74 fomr: 0.12 lr_plus: 5.29  acc: 0.83 
#>       fdr: 0.26 npv: 0.88  lr_minus: 0.27 dor: 19.85
#> 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