autoBagging (0.1.0)

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Learning to Rank Bagging Workflows with Metalearning.

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.

Maintainer: Vitor Cerqueira
Author(s): Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb], Joao Mendes-Moreira [ctb]

License: GPL (>= 2)

Uses: abind, caret, cluster, CORElearn, e1071, entropy, infotheo, lsr, MASS, minerva, party, rpart, xgboost, testthat

Released about 1 year ago.



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