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 <code><a rd-options="" href="/link/aggregations?package=mlr&version=2.10" data-mini-rdoc="mlr::aggregations">aggregations</a></code>. 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. Object members:
<dl class="dl-horizontal">
<dt>id [<code>character(1)</code>]</dt><dd>Name of the aggregation method.</dd>
<dt>name [<code>character(1)</code>]</dt><dd>Long name of the aggregation method.</dd>
<dt>properties [<code>character</code>]</dt><dd>Properties of the aggregation.</dd>
<dt>fun [<code>function(task, perf.test, perf.train, measure, group, pred)</code>]</dt><dd>Aggregation function.</dd>
</dl>

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.

Aggregation: Aggregation object.

Description

Arguments

See Also