makeCostMeasure: Creates a measure for non-standard misclassification costs.
In Najah-lshanableh/R-data-mining2: Machine Learning in R

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

1 2	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 “”.