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Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.

copied from cf-staging / r-mlrmbo
Type Size Name Uploaded Downloads Labels
conda 983.6 kB | win-64/r-mlrmbo-1.1.4-r35hda5aaf8_0.tar.bz2  4 years and 9 months ago 1031 main cf202003
conda 984.9 kB | win-64/r-mlrmbo-1.1.4-r36hda5aaf8_0.tar.bz2  4 years and 9 months ago 1028 main cf202003
conda 947.5 kB | osx-64/r-mlrmbo-1.1.4-r35h17f1fa6_0.tar.bz2  4 years and 9 months ago 321 main cf202003
conda 955.0 kB | osx-64/r-mlrmbo-1.1.4-r36h17f1fa6_0.tar.bz2  4 years and 9 months ago 322 main cf202003
conda 953.9 kB | linux-64/r-mlrmbo-1.1.4-r35hcdcec82_0.tar.bz2  4 years and 9 months ago 2945 main cf202003
conda 954.4 kB | linux-64/r-mlrmbo-1.1.4-r36hcdcec82_0.tar.bz2  4 years and 9 months ago 2937 main cf202003

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