BaggingRegress: Standard Bagging ensemble for regression problems.

In paobranco/UBL: An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks

Description

This function handles regression problems through ensemble learning. A given number of weak learners selected by the user are trained on bootstrap samples of the training data provided.

Usage

BaggingRegress(form, train, nmodels, learner, learner.pars,
               aggregation = "Average", quiet=TRUE)

Arguments

form	A formula describing the prediction problem.
train	A data frame containing the training (imbalanced) data set.
nmodels	A numeric indicating the number of models to train.
learner	The learning algorithm to be used as weak learner.
learner.pars	A named list with the parameters selected for the learner.
aggregation	charater specifying the method used for aggregating the results obtained by the individual learners. For now, the only method available is by averaging the models predictions.
quiet	logical specifying if development should be shown or not.Defaults to TRUE

Value

The function returns an object of class BagModel.

Author(s)

Paula Branco [e-mail], Rita Ribeiro [e-mail] and Luis Torgo [e-mail]

paobranco/UBL documentation built on May 6, 2021, 6:57 p.m.