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. (ROCMeasures) Created by calculateROCMeasures. (logical(1)) If TRUE a short paragraph with explanations of the used measures is printed additionally. (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:

Other roc: asROCRPrediction()

Other performance: ConfusionMatrix, calculateConfusionMatrix(), estimateRelativeOverfitting(), makeCostMeasure(), makeCustomResampledMeasure(), makeMeasure(), measures, performance(), setAggregation(), setMeasurePars()

## Examples

lrn = makeLearner("classif.rpart", predict.type = "prob")
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