A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
Version: | 0.1.0 |
Depends: | R (≥ 2.10) |
Imports: | cluster, xgboost, methods, e1071, rpart, abind, caret, MASS, entropy, lsr, CORElearn, infotheo, minerva, party |
Suggests: | testthat |
Published: | 2017-07-02 |
Author: | Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb], Joao Mendes-Moreira [ctb] |
Maintainer: | Vitor Cerqueira <cerqueira.vitormanuel at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
Citation: | autoBagging citation info |
Materials: | README |
CRAN checks: | autoBagging results |
Reference manual: | autoBagging.pdf |
Package source: | autoBagging_0.1.0.tar.gz |
Windows binaries: | r-devel: autoBagging_0.1.0.zip, r-release: autoBagging_0.1.0.zip, r-oldrel: autoBagging_0.1.0.zip |
OS X El Capitan binaries: | r-release: autoBagging_0.1.0.tgz |
OS X Mavericks binaries: | r-oldrel: autoBagging_0.1.0.tgz |
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