mlr

Description

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

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installed size is  5.6Mb
  sub-directories of 1Mb or more:
    R      2.0Mb
    data   2.3Mb

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installed size is  5.7Mb
  sub-directories of 1Mb or more:
    R      2.1Mb
    data   2.3Mb

Package suggested but not available for checking: ‘Rmpi’

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Package suggested but not available for checking: ‘Rmpi’

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