performanceMeasure {dcanr} | R Documentation

## Performance metrics to evaluate classification

### Description

Quantify the performance of a classification algorithm. Predictions and truth both have to be binary.

### Usage

    performanceMeasure(pred, obs, perf.method = "f.measure", ...)

### Arguments

`pred` | a logical or numeric, where 0 and FALSE represent control, and, 1 and TRUE represent cases
`obs` | a logical or numeric, where 0 and FALSE represent control, and, 1 and TRUE represent cases
`perf.method` | a character, specifying the method to use. Available methods can be accessed using `perfMethods`
`...` | additional parameters to methods. see details

### Details

The F-measure requires the beta parameter which can be specified using `f.beta` which defaults to 1 thereby computing the F1-measure.

### Value

a numeric, representing the performance

### See Also

`perfMethods`

### Examples

    pred <- sample(0:1, 100, replace = TRUE, prob = c(0.75, 0.25))
    obs <- sample(0:1, 100, replace = TRUE, prob = c(0.75, 0.25))

    #compute the F1 and F2 scores
    f1 <- performanceMeasure(pred, obs)
    f2 <- performanceMeasure(pred, obs, f.beta = 2)

[Package dcanr version 1.2.0 Index]