Instance-class

Instance.List-class

A base learner <code>Instance</code> is a combination of a base learner configuration and data partition. Instances constitute the major input into the cross-validation-based functions such as <code><a href="/link/Regression.CV.Batch.Fit?package=EnsembleBase&version=1.0.0" rd-options="" data-mini-rdoc="EnsembleBase::Regression.CV.Batch.Fit">Regression.CV.Batch.Fit</a></code>. An <code>Instance.List</code> is a collection of instances, along with the underlying definition of data partitions referenced in the instance objects. The function <code><a href="/link/make.instances?package=EnsembleBase&version=1.0.0" rd-options="" data-mini-rdoc="EnsembleBase::make.instances">make.instances</a></code> is a convenient function for generating an instance list from all permutations of a given list of base learner configurations and data partitions.

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.

Instance-class: Classes `"Instance"` and `"Instance.List"`

Description

Arguments

Objects from the Class

Methods

See Also

Examples