default_rf

This is a helper function that constructs a default random forest mlr3learners::LearnerRegrRanger which is for example used in <code>default_surrogate()</code>.

A modern and flexible approach to Bayesian Optimization / Model
Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox
providing both ready-to-use optimization algorithms as well as their fundamental
building blocks allowing for straightforward implementation of custom
algorithms. Single- and multi-objective optimization is supported as well as
mixed continuous, categorical and conditional search spaces. Moreover, using
'mlr3mbo' for hyperparameter optimization of machine learning models within the
'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use
optimization algorithms include Efficient Global Optimization by Jones et al.
(1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006)
<doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008)
<doi:10.1007/978-3-540-87700-4_78>.

Marc Becker

mlr3mbo

Flexible Bayesian Optimization

Lennart Schneider

Jakob Richter

Michel Lang

Bernd Bischl

Florian Pfisterer

Martin Binder

Sebastian Fischer

Michael H. Buselli

Wessel Dankers

Carlos Fonseca

Manuel Lopez-Ibanez

Luis Paquete

default_rf function

<dl><dt>noisy</dt>
<dd>(<code>logical(1)</code>) 
Whether the learner will be used in a noisy objective function scenario.
See <code>default_surrogate()</code>.</dd></dl>

Arguments

Default Random Forest — default_rf

<dl>

<dt>noisy</dt>
<dd>(<code>logical(1)</code>) 
Whether the learner will be used in a noisy objective function scenario.
See <code>default_surrogate()</code>.</dd>

</dl>

Default Random Forest

default_rf: Default Random Forest

Description

Usage

Value

Arguments

See Also