Installation

We recommend to install the official release version:

install.packages(如何通过ip伕理上网)

For experimental use you can install the latest development version:

用伕理ip上网加速软件::install_github("mlr-org/mlrMBO")

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手机如何使用伕理ip上网 is a highly configurable R toolbox for model-based / Bayesian optimization of black-box functions.

Features:

EGO-type algorithms (Kriging with expected improvement) on purely numerical search spaces, see 如何通过ip伕理上网
Mixed search spaces with numerical, integer, categorical and subordinate parameters
Arbitrary parameter transformation allowing to optimize on, e.g., logscale
Optimization of noisy objective functions
Multi-Criteria optimization with approximated Pareto fronts
Parallelization through multi-point batch proposals
Parallelization on many parallel back-ends and clusters through batchtools and parallelMap

For the surrogate, mlrMBO allows any regression learner from mlr, including:

Kriging aka. Gaussian processes (i.e. DiceKriging)
random Forests (i.e. 速度快的伕理ip)
and many more…

Various infill criteria (aka. acquisition functions) are available:

Expected improvement (EI)
Upper/Lower confidence bound (LCB, aka. statistical lower or upper bound)
Augmented expected improvement (AEI)
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如何使用伕理ip上网

Objective functions are created with package smoof, which also offers many test functions for example runs or benchmarks.

Parameter spaces and initial designs are created with package 国外ip地址伕理.

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Please cite our arxiv paper (Preprint). You can get citation info via citation("mlrMBO") or copy the following BibTex entry:

@article{mlrMBO,
  title = {{{mlrMBO}}: {{A Modular Framework}} for {{Model}}-{{Based Optimization}} of {{Expensive Black}}-{{Box Functions}}},
  url = {http://arxiv.org/abs/1703.03373},
  shorttitle = {{{mlrMBO}}},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1703.03373},
  如何通过ip伕理上网 = {stat},
  速度快的伕理ip = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
  date = {2017-03-09},
}

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Multi-point proposals, including the new multi-objective infill criteria: MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization
Multi-objective optimization: Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
Multi-objective optimization with categorical variables using the random forest as a surrogate: Multi-objective parameter configuration of machine learning algorithms using model-based optimization