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MultivariateRandomForest (version 1.1)

Multivariate Random Forest for Linearly Related Output Features

Description

In Random Forest, prediction has been done for single output feature, while linear relation between the output features has not been considered in other packages. In this package, using linear relation of the output features, a multivariate random forest prediction has been done, which can predict all the output features with one Random forest. If there is high correlation among the features, this MRF prediction gives better result than the individual RFs.

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Version

Install

install.packages('MultivariateRandomForest')

Monthly Downloads

246

Version

1.1

License

GPL-3

Maintainer

Raziur Rahman

Last Published

March 24th, 2016

Functions in MultivariateRandomForest (1.1)

split_node

Splitting Criteria of all the nodes of the tree
Multi_D_mod

Information Gain
spliting

Split of the Parent node
build_single_tree

Model of Single tree of Random Forest or Multivariate Random Forest
predicting

Prediction of testing sample in a node
Imputation

Imputation of a vector of number
CrossValidation

Matrix of Input and Output of Cross validation
single_tree_prediction

Prediction of Testing Samples for single tree
MultivariateRandomForest

Prediction using Random Forest or Multivariate Random Forest