CRAN
autoBagging 0.1.0
Learning to Rank Bagging Workflows with Metalearning
Released Jul 2, 2017 by Vitor Cerqueira
Dependencies
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.