KNN.Regression.Config-class

NNET.Regression.Config-class

RF.Regression.Config-class

SVM.Regression.Config-class

GBM.Regression.Config-class

PENREG.Regression.Config-class

BART.Regression.Config-class

These base learner configuration objects contain tuning parameters needed for training base learner algorithms. Names are identical to those used in implementation packages. See documentation for those packages for detailed definitions.

classes

ALL.Regression.Config-class

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

ALL.Regression.Config-class function

These objects are typically constructed via calls to <code><a href="/link/make.configs?package=EnsembleBase&version=1.0.0" rd-options="" data-mini-rdoc="EnsembleBase::make.configs">make.configs</a></code> and <code><a href="/link/make.instances?package=EnsembleBase&version=1.0.0" rd-options="" data-mini-rdoc="EnsembleBase::make.instances">make.instances</a></code>.

Objects from the Class

Class <code>"<links4class>Regression.Config</links4class>"</code>, directly.
Class <code>"<links4class>BaseLearner.Config</links4class>"</code>, by class "Regression.Config", distance 2.

Extends

Classes <code>"KNN.Regression.Config"</code>, <code>"NNET.Regression.Config"</code>, <code>"RF.Regression.Config"</code>, <code>"SVM.Regression.Config"</code>, <code>"GBM.Regression.Config"</code>, <code>"PENREG.Regression.Config"</code>, <code>"BART.Regression.Config"</code>

ALL.Regression.Config-class: Classes `"KNN.Regression.Config"`, `"NNET.Regression.Config"`, `"RF.Regression.Config"`, `"SVM.Regression.Config"`, `"GBM.Regression.Config"`, `"PENREG.Regression.Config"`, `"BART.Regression.Config"`

Description

Arguments

Objects from the Class

Extends

See Also