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 2.12.1 Index]