TuneResult

Container for results of hyperparameter tuning.
Contains the obtained point in search space, its performance values
and the optimization path which lead there.
Object members:<dl>
<dt>learner (Learner)</dt>
<dd>Learner that was optimized.</dd><dt>control (TuneControl)</dt>
<dd>Control object from tuning.</dd><dt>x (<a href="/link/list?package=mlr&version=2.19.1" data-mini-rdoc="mlr::list">list</a>)</dt>
<dd>Named list of hyperparameter values identified as optimal.
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 href="/link/numeric?package=mlr&version=2.19.1" data-mini-rdoc="mlr::numeric">numeric</a>)</dt>
<dd>Performance values for optimal <code>x</code>.</dd><dt>threshold (<a href="/link/numeric?package=mlr&version=2.19.1" data-mini-rdoc="mlr::numeric">numeric</a>)</dt>
<dd>Vector of finally found and used thresholds
if <code>tune.threshold</code> was enabled in TuneControl, otherwise not present and
hence <code>NULL</code>.</dd><dt>opt.path (ParamHelpers::OptPath)</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>.
If mlr option <code>on.error.dump</code> is <code>TRUE</code>, <code>OptPath</code> will have a <code>.dump</code> object
in its <code>extra</code> column which contains error dump traces from failed optimization evaluations.
It can be accessed by <code>getOptPathEl(opt.path)$extra$.dump</code>.</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

TuneResult function

Container for results of hyperparameter tuning.
Contains the obtained point in search space, its performance values
and the optimization path which lead there.
Object members:<dl>
<dt>learner (Learner)</dt>
<dd>Learner that was optimized.</dd>

<dt>control (TuneControl)</dt>
<dd>Control object from tuning.</dd>

<dt>x (<a href='https://rdrr.io/r/base/list.html'>list</a>)</dt>
<dd>Named list of hyperparameter values identified as optimal.
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 href='https://rdrr.io/r/base/numeric.html'>numeric</a>)</dt>
<dd>Performance values for optimal <code>x</code>.</dd>

<dt>threshold (<a href='https://rdrr.io/r/base/numeric.html'>numeric</a>)</dt>
<dd>Vector of finally found and used thresholds
if <code>tune.threshold</code> was enabled in TuneControl, otherwise not present and
hence <code>NULL</code>.</dd>

<dt>opt.path (ParamHelpers::OptPath)</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>.
If mlr option <code>on.error.dump</code> is <code>TRUE</code>, <code>OptPath</code> will have a <code>.dump</code> object
in its <code>extra</code> column which contains error dump traces from failed optimization evaluations.
It can be accessed by <code>getOptPathEl(opt.path)$extra$.dump</code>.</dd>


</dl>

Result of tuning. — TuneResult

Result of tuning.

TuneResult: Result of tuning.

Description

Arguments