BaseLearner.CV.FitObj-class

Regression.CV.FitObj-class

Classes for containing base learner CV 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.CV.FitObj-class function

<code>"BaseLearner.CV.FitObj"</code> is a virtual class: No objects may be created from it. <code>"Regression.CV.FitObj"</code> is the output of <code>Regression.CV.Fit</code> function.

Objects from the Class

<dl>
 <dt><code>fitobj.list</code>:</dt>
<dd>Object of class <code>"list"</code>, contains a list of objects of class <code>BaseLearner.Fit</code>, one per partition fold.</dd> <dt><code>partition</code>:</dt>
<dd>Object of class <code>"OptionalInteger"</code>, representing how data must be split across folds during cross-validation. This is typically the output of <code>generate.partition</code> function.</dd> <dt><code>filemethod</code>:</dt>
<dd>Object of class <code>"logical"</code>, determining whether to save individual estimation objects to file or not.</dd> In addition, Regression.CV.FitObj contains the following slot:
 <dt>pred</dt>
<dd>Object of class <code>"OptionalNumeric"</code>, containing the prediction from the CV fit object for training data. This slot is allowed to take on a "NULL" value to reduce excess memory use by large ensemble models.</dd> 
</dl>

Slots

No methods defined with class "BaseLearner.CV.FitObj" in the signature.

Methods

Alireza S. Mahani, Mansour T.A. Sharabiani

Author

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

<dl>
 <dt><code>fitobj.list</code>:</dt>
<dd>Object of class <code>"list"</code>, contains a list of objects of class <code>BaseLearner.Fit</code>, one per partition fold.</dd>

 <dt><code>partition</code>:</dt>
<dd>Object of class <code>"OptionalInteger"</code>, representing how data must be split across folds during cross-validation. This is typically the output of <code>generate.partition</code> function.</dd>

 <dt><code>filemethod</code>:</dt>
<dd>Object of class <code>"logical"</code>, determining whether to save individual estimation objects to file or not.</dd>

 In addition, Regression.CV.FitObj contains the following slot:
 <dt>pred</dt>
<dd>Object of class <code>"OptionalNumeric"</code>, containing the prediction from the CV fit object for training data. This slot is allowed to take on a "NULL" value to reduce excess memory use by large ensemble models.</dd>

 
</dl>

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

Description

Arguments

Objects from the Class

Slots

Methods

Author

See Also