Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, KNearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides crossvalidation wrappers to allow for downstream application of ensemble integration techniques, including besterror selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for retraining. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Package details 


Author  Alireza S. Mahani, Mansour T.A. Sharabiani 
Date of publication  20160913 22:30:52 
Maintainer  Alireza S. Mahani <[email protected]> 
License  GPL (>= 2) 
Version  1.0.2 
Package repository  View on CRAN 
Installation 
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