# calculateROCMeasures: Calculate receiver operator measures. In mlr: Machine Learning in R

 calculateROCMeasures R Documentation

### 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 class '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.

### Functions

• `print(ROCMeasures)`:

Other roc: `asROCRPrediction()`

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

### Examples

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