Regression.Integrator.Fit

Regression.Integrator.Fit-methods

Regression.Select.Fit

Regression.Select.Fit-methods

An object typically containing all configuration parameters of a particular integrator algorithm or operations.

object

print.level

Generic methods that can be extended and used in constructing integrator algorithms by other packages.

methods

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, K-Nearest 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 cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. 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.

Alireza S Mahani

EnsembleBase

Extensible Package for Parallel, Batch Training of Base Learners
        for Ensemble Modeling

Regression.Integrator.Fit-methods function

Generic Integrator Methods in Package <pkg>EnsembleBase</pkg>

Regression.Integrator.Fit-methods: Generic Integrator Methods in Package EnsembleBase

Description

Usage

Arguments