The aggregation can access all relevant information of
  the result after resampling and combine them into a
  single value. Though usually something very simple like
  taking the mean of the test set performances is done.

Aggregation

Interface to a large number of classification and
        regression techniques, including machine-readable parameter
        descriptions. Generic resampling, including cross-validation,
        bootstrapping and subsampling. Hyperparameter tuning with
        modern optimization techniques. Filter and wrapper methods for
        feature selection. Extension of basic learners with additional
        operations. Nested resampling.

Bernd Bischl

mlr: Machine Learning in R.

Aggregation function

An aggregation method reduces the performance values of the test (and possibly the training sets) to a single
value. To see all possible, implemented aggregations look at <code><a href="/link/aggregations?package=mlr&version=1.1-18" rd-options="" data-mini-rdoc="mlr::aggregations">aggregations</a></code>.

Aggregation: An aggregation method reduces the performance values of the test (and possibly the training sets) to a single value. To see all possible, implemented aggregations look at `aggregations`.

Description

Arguments

Details