Automated Intuitive Package for Meta-Ensemble Learning

Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.


Reference manual

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0.1.0 by Ajay Arunachalam, a year ago

Browse source code at

Authors: Ajay Arunachalam

Documentation:   PDF Manual  

GPL (>= 2) license

Imports caret, ggplot2, graphics, e1071, gbm, randomForest

Depends on gridExtra

Suggests knitr, R.rsp

See at CRAN