R: Creates a measure for non-standard misclassification costs.

makeCostMeasure {mlr} | R Documentation

## Creates a measure for non-standard misclassification costs.

### Description

Creates a cost measure for non-standard classification error costs.

### Usage

    makeCostMeasure(
      id = "costs",
      minimize = TRUE,
      costs,
      combine = mean,
      best = NULL,
      worst = NULL,
      name = id,
      note = ""
    )

### Arguments

`id` | (`character(1)`) Name of measure. Default is “costs”.
`minimize` | (`logical(1)`) Should the measure be minimized? Otherwise you are effectively specifying a benefits matrix. Default is `TRUE`.
`costs` | (matrix) Matrix of misclassification costs. Rows and columns have to be named with class labels, order does not matter. Rows indicate true classes, columns predicted classes.
`combine` | (`function`) How to combine costs over all cases for a SINGLE test set? Note this is not the same as the `aggregate` argument in makeMeasure You can set this as well via setAggregation, as for any measure. Default is mean.
`best` | (`numeric(1)`) Best obtainable value for measure. Default is -`Inf` or `Inf`, depending on `minimize`.
`worst` | (`numeric(1)`) Worst obtainable value for measure. Default is `Inf` or -`Inf`, depending on `minimize`.
`name` | (character) Name of the measure. Default is `id`.
`note` | (character) Description and additional notes for the measure. Default is “”.

### Value

Measure.

### See Also

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

[Package mlr version 2.17.1 Index]