RegressionSelectPred-class

Union of classes <code>"NULL"</code>, <code>"numeric"</code> and <code>"matrix"</code> to hold prediction output of <code>Select</code> operations based on generic function <code>Regression.Select.Fit</code>. Class <code>NULL</code> is included to allow methods to save memory by not returning the prediction, espeically when a highe-level wrapper takes responsibility for holding a global copy of all prediction results. The "numeric" and "matrix" classes allow for a single predictor or multiple predictors to be produced by a <code>Select</code> operation.

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 Mahani

EnsembleBase

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

RegressionSelectPred-class function

A virtual Class: No objects may be created from it.

Objects from the Class

No methods defined with class "RegressionSelectPred" in the signature.

Methods

Alireza S. Mahani, Mansour T.A. Sharabiani

RegressionSelectPred-class: Class `"RegressionSelectPred"`

Description

Arguments

Objects from the Class

Methods

Author

See Also