UBL: An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks

Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.

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AuthorPaula Branco [aut, cre], Rita Ribeiro [aut, ctb], Luis Torgo [aut, ctb]
Date of publication2016-07-13 16:17:09
MaintainerPaula Branco <paobranco@gmail.com>
LicenseGPL (>= 2)

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CNNClassif Man page
ENNClassif Man page
GaussNoiseClassif Man page
GaussNoiseRegress Man page
ImbC Man page
ImbR Man page
ImpSampClassif Man page
ImpSampRegress Man page
NCLClassif Man page
OSSClassif Man page
phi Man page
phi.control Man page
RandOverClassif Man page
RandOverRegress Man page
RandUnderClassif Man page
RandUnderRegress Man page
SmoteClassif Man page
SmoteRegress Man page
TomekClassif Man page
UBL-package Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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