mlrMBO

Package website: 国外ip地址伋理 | dev

Model-based optimization with mlr.

• Documentation
• Issues, Requests and Bug Tracker

Installation

We recommend to install the official release version:

install.packages(如何通过ip伋理上网)

For experimental use you can install the latest development version:

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

快连加速器图标

手机如何使用伋理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伋理上网
• 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. 速度快的伋理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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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地址伋理.

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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伋理上网 = {stat},
  速度快的伋理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