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 = "")(`character(1)`) Short name of measure.
(`character(1)`) Short name of aggregation.
(`logical(1)`) Should the measure be minimized? Default is `TRUE`.
([character]) Set of measure properties. For a list of values see [Measure]. Default is `character(0)`.
(`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`.
The task.
Grouping of resampling iterations. This encodes whether specific iterations 'belong together' (e.g. repeated CV).
Prediction object.
See below.
([list]) List of extra arguments which will always be passed to `fun`. Default is empty list.
(`numeric(1)`) Best obtainable value for measure. Default is -`Inf` or `Inf`, depending on `minimize`.
(`numeric(1)`) Worst obtainable value for measure. Default is `Inf` or -`Inf`, depending on `minimize`.
(`character(1)`) Long name of measure. Default is `measure.id`.
(`character(1)`) Long name of the aggregation. Default is `aggregation.id`.
([character]) Description and additional notes for the measure. Default is “”.
Other performance: ConfusionMatrix,
calculateConfusionMatrix,
calculateROCMeasures,
estimateRelativeOverfitting,
makeCostMeasure, makeMeasure,
measures, performance,
setAggregation,
setMeasurePars