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. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

Bernd Bischl

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=2.2" 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

See Also