In berndbischl/mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

mlrMBO Tutorial

mlrMBO is a framework for the (sequential) Model Based parameter Optimization. The goal is to optimize numeric or discrete influence parameters of a non-linear black box function like an industrial simulator or a time-consuming algorithm.

In the following we provide an in-depth introduction to mlrMBO. An introductory example serves as a quickstart guide. Note that our focus is on your comprehension of the basic functions and applications. For detailed technical information and manual pages, please refer to the package's manual pages. They are regularly updated and reflect the documentation of the current packages on CRAN.

1. Quickstart
2. In Depth Introduction
3. Further advanced topics:
4. Parallelization Make use of multicore CPUs and other distributed computing methods.

berndbischl/mlrMBO documentation built on Oct. 11, 2022, 1:44 p.m.