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

makeCostMeasure {mlr}

R Documentation

Creates a measure for non-standard misclassification costs.

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

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

`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 “”.