mlr_result_assigners

A simple mlr3misc::Dictionary storing objects of class ResultAssigner.
Each acquisition function has an associated help page, see <code>mlr_result_assigners_[id]</code>.
For a more convenient way to retrieve and construct an acquisition function, see <code>ras()</code>.

datasets

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>.

Lennart Schneider

mlr3mbo

Flexible Bayesian Optimization

Jakob Richter

Marc Becker

Michel Lang

Bernd Bischl

Florian Pfisterer

Martin Binder

Sebastian Fischer

Michael H. Buselli

Wessel Dankers

Carlos Fonseca

Manuel Lopez-Ibanez

Luis Paquete

mlr_result_assigners function

<a href="https://r6.r-lib.org/reference/R6Class.html">R6::R6Class</a> object inheriting from mlr3misc::Dictionary.

Format

See mlr3misc::Dictionary.

Methods

Dictionary of Result Assigners — mlr_result_assigners

<a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class</a> object inheriting from mlr3misc::Dictionary.

Dictionary of Result Assigners

mlr_result_assigners: Dictionary of Result Assigners

Description

Arguments

Format

Methods

See Also

Examples