BaseLearner.FitObj-class

Regression.FitObj-class

Base class templates for containing base learner training output.

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

BaseLearner.FitObj-class function

<code>"BaseLearner.FitObj"</code> is a virtual class: No objects may be created from it. <code>"Regression.FitObj"</code> is a base class for objects representing trained models for individual base learners.

Objects from the Class

<dl>
 <dt><code>config</code>:</dt>
<dd>Object of class <code>"BaseLearner.Config"</code>; often one of the derived configuration classes belonging to a particular base learner.</dd> For Regression.FitObj, we have the following additional fields:
 <dt><code>est</code>:</dt>
<dd>Object of class <code>"RegressionEstObj"</code>, typically containing the low-level list coming out of the training algorithm. If <code>filemethod=TRUE</code> during the fit, this object will be of class "character", containing the filepath to where the estimation object is stored.</dd> <dt><code>pred</code>:</dt>
<dd>Object of class <code>"OptionalNumeric"</code>, fitted values of the model for the training data. It is allowed to be "NULL" in order to reduce memory footrpint during cross-validated ensemble methods.</dd> 
</dl>

Slots

No methods defined with these classes in their signature.

Methods

Alireza S. Mahani, Mansour T.A. Sharabiani

Author

Classes "BaseLearner.FitObj" and "Regression.FitObj" — BaseLearner.FitObj-class

<dl>
 <dt><code>config</code>:</dt>
<dd>Object of class <code>"BaseLearner.Config"</code>; often one of the derived configuration classes belonging to a particular base learner.</dd>

 For Regression.FitObj, we have the following additional fields:
 <dt><code>est</code>:</dt>
<dd>Object of class <code>"RegressionEstObj"</code>, typically containing the low-level list coming out of the training algorithm. If <code>filemethod=TRUE</code> during the fit, this object will be of class "character", containing the filepath to where the estimation object is stored.</dd>

 <dt><code>pred</code>:</dt>
<dd>Object of class <code>"OptionalNumeric"</code>, fitted values of the model for the training data. It is allowed to be "NULL" in order to reduce memory footrpint during cross-validated ensemble methods.</dd>

 
</dl>

BaseLearner.FitObj-class: Classes `"BaseLearner.FitObj"` and `"Regression.FitObj"`

Description

Arguments

Objects from the Class

Slots

Methods

Author

See Also