EnsembleBase: Extensible Package for Parallel, Batch Training of Base Learners for Ensemble Modeling

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.

AuthorAlireza S. Mahani, Mansour T.A. Sharabiani
Date of publication2016-09-13 22:30:52
MaintainerAlireza S. Mahani <alireza.s.mahani@gmail.com>
LicenseGPL (>= 2)
Version1.0.2

View on CRAN

Functions

BART.Regression.Config-class Man page
BART.Regression.FitObj-class Man page
BaseLearner.Batch.FitObj-class Man page
BaseLearner.Config-class Man page
BaseLearner.CV.Batch.FitObj-class Man page
BaseLearner.CV.FitObj-class Man page
BaseLearner.Fit Man page
BaseLearner.Fit,BART.Regression.Config-method Man page
BaseLearner.Fit,GBM.Regression.Config-method Man page
BaseLearner.Fit,KNN.Regression.Config-method Man page
BaseLearner.Fit-methods Man page
BaseLearner.Fit,NNET.Regression.Config-method Man page
BaseLearner.FitObj-class Man page
BaseLearner.Fit,PENREG.Regression.Config-method Man page
BaseLearner.Fit,RF.Regression.Config-method Man page
BaseLearner.Fit,SVM.Regression.Config-method Man page
extract.baselearner.name Man page
GBM.Regression.Config-class Man page
GBM.Regression.FitObj-class Man page
generate.partition Man page
generate.partitions Man page
Instance-class Man page
Instance.List-class Man page
KNN.Regression.Config-class Man page
KNN.Regression.FitObj-class Man page
load.object Man page
make.configs Man page
make.configs.bart.regression Man page
make.configs.gbm.regression Man page
make.configs.knn.regression Man page
make.configs.nnet.regression Man page
make.configs.penreg.regression Man page
make.configs.rf.regression Man page
make.configs.svm.regression Man page
make.instances Man page
NNET.Regression.Config-class Man page
NNET.Regression.FitObj-class Man page
OptionalCharacter-class Man page
OptionalInteger-class Man page
OptionalNumeric-class Man page
PENREG.Regression.Config-class Man page
PENREG.Regression.FitObj-class Man page
plot.Regression.Batch.FitObj Man page
plot.Regression.CV.Batch.FitObj Man page
predict.Regression.Batch.FitObj Man page
predict.Regression.CV.Batch.FitObj Man page
predict.Regression.CV.FitObj Man page
Regression.Batch.Fit Man page
Regression.Batch.FitObj-class Man page
Regression.Config-class Man page
Regression.CV.Batch.Fit Man page
Regression.CV.Batch.FitObj-class Man page
Regression.CV.Fit Man page
Regression.CV.FitObj-class Man page
RegressionEstObj-class Man page
regression.extract.response Man page
Regression.FitObj-class Man page
Regression.Integrator.Config-class Man page
Regression.Integrator.Fit Man page
Regression.Integrator.Fit-methods Man page
Regression.Integrator.FitObj-class Man page
Regression.Select.Config-class Man page
Regression.Select.Fit Man page
Regression.Select.Fit-methods Man page
Regression.Select.FitObj-class Man page
RegressionSelectPred-class Man page
RF.Regression.Config-class Man page
RF.Regression.FitObj-class Man page
rmse.error Man page
servo Man page
SVM.Regression.Config-class Man page
SVM.Regression.FitObj-class Man page
validate Man page
validate-methods Man page
validate,Regression.Batch.FitObj-method Man page
validate,Regression.CV.Batch.FitObj-method Man page

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