mlr (version 1.1-18)

aggregations: Aggregation methods.

Description

• test.mean

{ Mean of performance values on test sets.} test.sd{ Standard deviation of performance values on test sets.} test.median{ Median of performance values on test sets.} test.min{ Minimum of performance values on test sets.} test.max{ Maximum of performance values on test sets.} test.sum{ Sum of performance values on test sets.} train.mean{ Mean of performance values on training sets.} train.sd{ Standard deviation of performance values on training sets.} train.median{ Median of performance values on training sets.} train.min{ Minimum of performance values on training sets.} train.max{ Maximum of performance values on training sets.} train.sum{ Sum of performance values on training sets.} b632{ Aggregation for B632 bootstrap.} b632plus{ Aggregation for B632+ bootstrap.} testgroup.mean{ Performance values on test sets are grouped according to resampling method. The mean for very group is calculated, then the mean of those means. Mainly used for repeated CV.}

Usage

test.mean
  test.sd
  test.median
  test.min
  test.max
  test.sum
  test.sqrt.of.mean
  train.mean
  train.sd
  train.median
  train.min
  train.max
  train.sum
  b632
  b632plus
  testgroup.mean

docType

data

format

List of 2 $ id : chr "test.mean" $ fun:function (task, perf.test, perf.train, measure, group, pred) - attr(*, "class")= chr "Aggregation"

See Also

Aggregation