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, ...)

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.

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.

Methods (by generic)

print:

Examples

Run this code

lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
calculateROCMeasures(pred)