mlr (version 2.17.0)

calculateROCMeasures: Calculate receiver operator measures.

Description

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.

Usage

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

Other roc: asROCRPrediction()

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

Examples

# NOT RUN {
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
calculateROCMeasures(pred)
# }