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Advanced Tuning Methods and Black Box Optimization

MCML Authors

Abstract

Automated tuning can be error prone and it is very likely that models will crash in the tuning process, it is therefore essential to have reliable methods of encapsulating errors to prevent large experiments from failing and losing intermediate results. This chapter therefore begins by introducing fallback learners and encapsulation methods, which are returned to in ‘Advanced Technical Aspects of mlr3’.<br>Models can be tuned with respect to one or multiple measures. In general when tuning to multiple measures there will be a trade-off between them and therefore there will not be one optimal hyperparameter configuration, instead the aim is to estimate configurations that are not Pareto-dominated by any other. This chapter introduces multi-objective tuning and concepts including Pareto optimality.<br>Some tuning methods are more advanced than others, including Hyperband and Bayesian optimization. Hyperband is a multi-fidelity tuner that makes use of fidelity parameters, which provide a tradeoff between model runtime and performance accuracy. Bayesian optimization is a sample-efficient black-box optimization algorithm that is highly flexible and allows user fine-grained control over tuning large search spaces. This chapter introduces mlr3hyperband and the concept of fidelity parameters, and then mlr3mbo and bbotk to discuss black-box optimization and Bayesian optimization.

article

Applied Machine Learning Using mlr3 in R

II.5. Jan. 2024.

Authors

L. Schneider • M. Becker

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: SB24

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