Calculate the absolute number of correct/incorrect classifications and the following evaluation measures:

  • 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)

  • for 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

For details on the used measures see measures and also https://en.wikipedia.org/wiki/Receiver_operating_characteristic.

The element for the false omission rate in the resulting object is not called for but fomr since for should never be used as a variable name in an object.

calculateROCMeasures(pred)

# S3 method for ROCMeasures
print(x, abbreviations = TRUE, digits = 2, ...)

Arguments

pred

(Prediction)
Prediction object.

x

(ROCMeasures)
Created by calculateROCMeasures.

abbreviations

(logical(1))
If TRUE a short paragraph with explanations of the used measures is printed additionally.

digits

(integer(1))
Number of digits the measures are rounded to.

...

(any)
Currently not used.

Value

(ROCMeasures). 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.

Methods (by generic)

  • print:

See also

Examples

lrn = makeLearner("classif.rpart", predict.type = "prob") fit = train(lrn, sonar.task) pred = predict(fit, task = sonar.task) calculateROCMeasures(pred)
#> predicted #> true M R #> M 95 16 tpr: 0.86 fnr: 0.14 #> R 10 87 fpr: 0.1 tnr: 0.9 #> ppv: 0.9 for: 0.16 lrp: 8.3 acc: 0.88 #> fdr: 0.1 npv: 0.84 lrm: 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) #> for - 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