MBOSingleObjResult

<ul>
<li>x [<code>list</code>]Named list of proposed optimal parameters.</li>
<li>y [<code>numeric(1)</code>]Value of objective function at <code>x</code>,
 either from evals during optimization or from requested final evaluations,
 if those were greater than 0.</li>
<li>best.ind [<code>numeric(1)</code>]Index of <code>x</code> in the opt.path.</li>
<li>opt.path [<code><a rd-options="ParamHelpers" href="/link/OptPath?package=mlrMBO&version=1.1.5&to=ParamHelpers" data-mini-rdoc="ParamHelpers::OptPath">OptPath</a></code>]Optimization path.
 Includes all evaluated points and additional information as documented in <a rd-options="" href="/link/mbo_OptPath?package=mlrMBO&version=1.1.5" data-mini-rdoc="mlrMBO::mbo_OptPath">mbo_OptPath</a>.
 You can convert it via <code>as.data.frame</code>.</li>
<li>resample.results [List of <code><a rd-options="mlr" href="/link/ResampleResult?package=mlrMBO&version=1.1.5&to=mlr" data-mini-rdoc="mlr::ResampleResult">ResampleResult</a></code>]List of the desired <code>resample.results</code> if <code>resample.at</code> is set in <code>makeMBOControl</code>.</li>
<li>final.state [<code>character</code>] The final termination state. Gives information why the optimization ended. Possible values are
 <dl class="dl-horizontal">
 <dt>term.iter</dt><dd>Maximal number of iterations reached.</dd>
 <dt>term.time</dt><dd>Maximal running time exceeded.</dd>
 <dt>term.exectime</dt><dd>Maximal execution time of function evaluations reached.</dd>
 <dt>term.yval</dt><dd>Target function value reached.</dd>
 <dt>term.fevals</dt><dd>maximal number of function evaluations reached.</dd>
 <dt>term.custom</dt><dd>Terminated due to custom, user-defined termination condition.</dd>
 </dl></li>
<li>models [List of <code><a rd-options="mlr" href="/link/WrappedModel?package=mlrMBO&version=1.1.5&to=mlr" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code>]List of saved regression models if <code>store.model.at</code> is set in <code>makeMBOControl</code>. The default is that it contains the model generated after the last iteration.</li>
<li>control [<code>MBOControl</code>] Control object used in optimization</li>
</ul>

Flexible and comprehensive R toolbox for model-based optimization
('MBO'), also known as Bayesian optimization. It implements the Efficient
Global Optimization Algorithm and is designed for both single- and multi-
objective optimization with mixed continuous, categorical and conditional
parameters. The machine learning toolbox 'mlr' provide dozens of regression
learners to model the performance of the target algorithm with respect to
the parameter settings. It provides many different infill criteria to guide
the search process. Additional features include multi-point batch proposal,
parallel execution as well as visualization and sophisticated logging
mechanisms, which is especially useful for teaching and understanding of
algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
single components can be easily replaced or adapted by the user for specific
use cases.

Jakob Richter

mlrMBO

Bayesian Optimization and Model-Based Optimization of Expensive
Black-Box Functions

Bernd Bischl

Jakob Bossek

Daniel Horn

Michel Lang

Janek Thomas

MBOSingleObjResult function

<ul>
<li>x [<code>list</code>]Named list of proposed optimal parameters.</li>
<li>y [<code>numeric(1)</code>]Value of objective function at <code>x</code>,
 either from evals during optimization or from requested final evaluations,
 if those were greater than 0.</li>
<li>best.ind [<code>numeric(1)</code>]Index of <code>x</code> in the opt.path.</li>
<li>opt.path [<code><a rd-options='ParamHelpers' href='OptPath'>OptPath</a></code>]Optimization path.
 Includes all evaluated points and additional information as documented in <a rd-options='' href='mbo_OptPath'>mbo_OptPath</a>.
 You can convert it via <code>as.data.frame</code>.</li>
<li>resample.results [List of <code><a rd-options='mlr' href='ResampleResult'>ResampleResult</a></code>]List of the desired <code>resample.results</code> if <code>resample.at</code> is set in <code>makeMBOControl</code>.</li>
<li>final.state [<code>character</code>] The final termination state. Gives information why the optimization ended. Possible values are
 <dl class='dl-horizontal'>
 <dt>term.iter</dt><dd>Maximal number of iterations reached.</dd>
 <dt>term.time</dt><dd>Maximal running time exceeded.</dd>
 <dt>term.exectime</dt><dd>Maximal execution time of function evaluations reached.</dd>
 <dt>term.yval</dt><dd>Target function value reached.</dd>
 <dt>term.fevals</dt><dd>maximal number of function evaluations reached.</dd>
 <dt>term.custom</dt><dd>Terminated due to custom, user-defined termination condition.</dd>
 </dl></li>
<li>models [List of <code><a rd-options='mlr' href='WrappedModel'>WrappedModel</a></code>]List of saved regression models if <code>store.model.at</code> is set in <code>makeMBOControl</code>. The default is that it contains the model generated after the last iteration.</li>
<li>control [<code>MBOControl</code>] Control object used in optimization</li>
</ul>

Single-Objective result object. — MBOSingleObjResult

Single-Objective result object.

MBOSingleObjResult: Single-Objective result object.

Description

Arguments