metaEnsembleR

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 https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/ 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.

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