TuneMultiCritResult

Container for results of hyperparameter tuning.
Contains the obtained pareto set and front
and the optimization path which lead there.
Object members:<dl class="dl-horizontal">
<dt>learner (<a rd-options="" href="/link/Learner?package=mlr&version=2.17.0" data-mini-rdoc="mlr::Learner">Learner</a>)</dt><dd>Learner that was optimized.</dd>
<dt>control (<a rd-options="" href="/link/TuneControl?package=mlr&version=2.17.0" data-mini-rdoc="mlr::TuneControl">TuneControl</a>)</dt><dd>Control object from tuning.</dd>
<dt>x (<a rd-options="" href="/link/list?package=mlr&version=2.17.0" data-mini-rdoc="mlr::list">list</a>)</dt><dd>List of lists of non-dominated hyperparameter settings in pareto set.
Note that when you have trafos on some of your params, <code>x</code> will always be
on the TRANSFORMED scale so you directly use it.</dd>
<dt>y (<a rd-options="" href="/link/matrix?package=mlr&version=2.17.0" data-mini-rdoc="mlr::matrix">matrix</a>)</dt><dd>Pareto front for <code>x</code>.</dd>
<dt>threshold</dt><dd>Currently <code>NULL</code>.</dd>
<dt>opt.path (<a rd-options="ParamHelpers:OptPath" href="/link/ParamHelpers%3A%3AOptPath?package=mlr&version=2.17.0&to=ParamHelpers%3AOptPath" data-mini-rdoc="ParamHelpers:OptPath::ParamHelpers::OptPath">ParamHelpers::OptPath</a>)</dt><dd>Optimization path which lead to <code>x</code>.
Note that when you have trafos on some of your params, the opt.path always contains the
UNTRANSFORMED values on the original scale. You can simply call <code>trafoOptPath(opt.path)</code> to
transform them, or, <code>as.data.frame{trafoOptPath(opt.path)}</code></dd>
<dt>ind (<code>integer(n)</code>)</dt><dd>Indices of Pareto optimal params in <code>opt.path</code>.</dd>
<dt>measures [(list of) <a rd-options="" href="/link/Measure?package=mlr&version=2.17.0" data-mini-rdoc="mlr::Measure">Measure</a>)</dt><dd>Performance measures.</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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

TuneMultiCritResult function

Container for results of hyperparameter tuning.
Contains the obtained pareto set and front
and the optimization path which lead there.
Object members:<dl class='dl-horizontal'>
<dt>learner (<a rd-options='' href='Learner'>Learner</a>)</dt><dd>Learner that was optimized.</dd>
<dt>control (<a rd-options='' href='TuneControl'>TuneControl</a>)</dt><dd>Control object from tuning.</dd>
<dt>x (<a rd-options='' href='list'>list</a>)</dt><dd>List of lists of non-dominated hyperparameter settings in pareto set.
Note that when you have trafos on some of your params, <code>x</code> will always be
on the TRANSFORMED scale so you directly use it.</dd>
<dt>y (<a rd-options='' href='matrix'>matrix</a>)</dt><dd>Pareto front for <code>x</code>.</dd>
<dt>threshold</dt><dd>Currently <code>NULL</code>.</dd>
<dt>opt.path (<a rd-options='ParamHelpers:OptPath' href='ParamHelpers::OptPath'>ParamHelpers::OptPath</a>)</dt><dd>Optimization path which lead to <code>x</code>.
Note that when you have trafos on some of your params, the opt.path always contains the
UNTRANSFORMED values on the original scale. You can simply call <code>trafoOptPath(opt.path)</code> to
transform them, or, <code>as.data.frame{trafoOptPath(opt.path)}</code></dd>
<dt>ind (<code>integer(n)</code>)</dt><dd>Indices of Pareto optimal params in <code>opt.path</code>.</dd>
<dt>measures [(list of) <a rd-options='' href='Measure'>Measure</a>)</dt><dd>Performance measures.</dd>
</dl>

Result of multi-criteria tuning. — TuneMultiCritResult

Result of multi-criteria tuning.

TuneMultiCritResult: Result of multi-criteria tuning.

Description

Arguments