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 DOI: 10.32614/CRAN.package.autoBagging 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=autoBagging to link to this page.
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