BaseLearner.Config-class

Regression.Config-class

Base classes in the configuration class hierarchy.

classes

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

BaseLearner.Config-class function

<code>"BaseLearner.Config"</code> is a virtual Class: No objects may be created from it. <code>"Regression.Config"</code> is a base class for configuration classes of specific base learners, such as <code><links4class>SVM.Regression.Config</links4class></code>; therefore, there is typically no need to generate objects from this base class directly.

Objects from the Class

Class <code>"<links4class>Regression.Config</links4class>"</code> extends class <code>"<links4class>BaseLearner.Config</links4class>"</code>, directly.

Extends

No methods defined with class "BaseLearner.Config" or "Regression.Config" in the signature.

BaseLearner.Config-class: Classes `"BaseLearner.Config"`, `"Regression.Config"`

Description

Arguments

Objects from the Class

Extends

Methods

See Also

Examples