addRRMeasure

[<code>ResampleResult</code>]
The result of <code><a rd-options="" href="/link/resample?package=mlr&version=2.10" data-mini-rdoc="mlr::resample">resample</a></code> run with <code>keep.pred = TRUE</code>.

[<code><a rd-options="" href="/link/Measure?package=mlr&version=2.10" data-mini-rdoc="mlr::Measure">Measure</a></code> | list of <code><a rd-options="" href="/link/Measure?package=mlr&version=2.10" data-mini-rdoc="mlr::Measure">Measure</a></code>]
Performance measure(s) to evaluate.
Default is the default measure for the task, see here <code><a rd-options="" href="/link/getDefaultMeasure?package=mlr&version=2.10" data-mini-rdoc="mlr::getDefaultMeasure">getDefaultMeasure</a></code>.

measures

Adds new measures to an existing <code>ResampleResult</code>.

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

addRRMeasure function

[<code>ResampleResult</code>]
The result of <code><a rd-options='' href='resample'>resample</a></code> run with <code>keep.pred = TRUE</code>.

[<code><a rd-options='' href='Measure'>Measure</a></code> | list of <code><a rd-options='' href='Measure'>Measure</a></code>]
Performance measure(s) to evaluate.
Default is the default measure for the task, see here <code><a rd-options='' href='getDefaultMeasure'>getDefaultMeasure</a></code>.

addRRMeasure: Compute new measures for existing ResampleResult

Description

Usage

Arguments

Value

See Also