makeCustomResampledMeasure: Construct your own resampled performance measure.
In guillermozbta/mir: Machine Learning in R

Description Usage Arguments Value See Also

Construct your own performance measure, used after resampling. Note that individual training / test set performance values will be set to NA, you only calculate an aggregated value. If you can define a function that makes sense for every single training / test set, implement your own Measure.

makeCustomResampledMeasure(measure.id, aggregation.id, minimize = TRUE,
  properties = character(0L), fun, extra.args = list(), best = NULL,
  worst = NULL, measure.name = measure.id,
  aggregation.name = aggregation.id, note = "")

`measure.id`	[`character(1)`] Short name of measure.
`aggregation.id`	[`character(1)`] Short name of aggregation.
`minimize`	[`logical(1)`] Should the measure be minimized? Default is `TRUE`.
`properties`	[`character`] Set of measure properties. For a list of values see `Measure`. Default is `character(0)`.
`fun`	[`function(task, group, pred, extra.args)`] Calculates performance value from `ResamplePrediction` object. For rare cases you can also use the task, the grouping or the extra arguments `extra.args`. `task` [`Task`] The task. `group` [`factor`] Grouping of resampling iterations. This encodes whether specific iterations 'belong together' (e.g. repeated CV). `pred` [`Prediction`] Prediction object. `extra.args` [`list`] See below.
`extra.args`	[`list`] List of extra arguments which will always be passed to `fun`. Default is empty list.
`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`.
`measure.name`	[`character(1)`] Long name of measure. Default is `measure.id`.
`aggregation.name`	[`character(1)`] Long name of the aggregation. Default is `aggregation.id`.
`note`	[`character`] Description and additional notes for the measure. Default is “”.