MBOMultiObjResult

<ul>
<li>pareto.front [<code>matrix</code>]Pareto front of all evaluated points.</li>
<li>pareto.set [<code>list</code> of <code>list</code>s]Pareto set of all evaluated points.</li>
<li>pareto.inds [<code>numeric</code>]Indices of the Pareto-optimal points in the opt.path</li>
<li>opt.path [<code><a rd-options="ParamHelpers" href="/link/OptPath?package=mlrMBO&version=1.1.2&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=1.1.2" data-mini-rdoc="mlrMBO::mbo_OptPath">mbo_OptPath</a>.
 You can convert it via <code>as.data.frame</code>.</li>
<li>final.state [<code>character</code>] The final termination state. Gives information why the optimization ended</li>
<li>models [List of <code><a rd-options="mlr" href="/link/WrappedModel?package=mlrMBO&version=1.1.2&to=mlr" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code>]List of saved regression models.</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

MBOMultiObjResult function

<ul>
<li>pareto.front [<code>matrix</code>]Pareto front of all evaluated points.</li>
<li>pareto.set [<code>list</code> of <code>list</code>s]Pareto set of all evaluated points.</li>
<li>pareto.inds [<code>numeric</code>]Indices of the Pareto-optimal points 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>final.state [<code>character</code>] The final termination state. Gives information why the optimization ended</li>
<li>models [List of <code><a rd-options='mlr' href='WrappedModel'>WrappedModel</a></code>]List of saved regression models.</li>
<li>control[<code>MBOControl</code>] Control object used in optimization</li>
</ul>

Multi-Objective result object. — MBOMultiObjResult

Multi-Objective result object.

MBOMultiObjResult: Multi-Objective result object.

Description

Arguments