BaseLearner.Fit-methods

BaseLearner.Fit

BaseLearner.Fit,GBM.Regression.Config-method

BaseLearner.Fit,KNN.Regression.Config-method

BaseLearner.Fit,NNET.Regression.Config-method

BaseLearner.Fit,RF.Regression.Config-method

BaseLearner.Fit,SVM.Regression.Config-method

BaseLearner.Fit,PENREG.Regression.Config-method

BaseLearner.Fit,BART.Regression.Config-method

An object of class <code><links4class>BaseLearner.Config</links4class></code> (must be a concrete implementation such as <code><links4class>KNN.Regression.Config</links4class></code>).

object

Formula object expressing response and covariates.

formula

Data frame containing response and covariates.

data

Filepath to save the estimation object to. If <code>NULL</code>, estimation object will not be saved to a file.

tmpfile

Controlling verbosity level during fitting.

print.level

Arguments to be passed to/from other methods.

Each base learner must provide its concrete implementation of this generic method.

methods

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.

BaseLearner.Fit-methods: Generic S4 Method for Fitting Base Learners

Description

Usage

Arguments