autoBagging 0.1.0

Learning to Rank Bagging Workflows with Metalearning

Released Jul 2, 2017 by Vitor Cerqueira

This package cannot yet be used with Renjin it depends on other packages which are not available: xgboost 0.71.2 and caret 6.0-80


xgboost 0.71.2 caret 6.0-80 e1071 1.7-0 cluster 2.0.7-1 rpart 4.1-13 CORElearn 1.52.1 party 1.3-1 minerva 1.4.7 abind 1.4-3 infotheo 1.2.0 lsr 0.5 MASS 7.3-50 entropy 1.2.1

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.



View GitHub Mirror

Release History