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 = ""
)

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 “”.