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Prediction Improvement using Optimal Scaling on Random Forest Models for Highly Categorical Data

by Saurabh Mangal, Aditya Shankar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 3
Year of Publication: 2014
Authors: Saurabh Mangal, Aditya Shankar
10.5120/18895-0183

Saurabh Mangal, Aditya Shankar . Prediction Improvement using Optimal Scaling on Random Forest Models for Highly Categorical Data. International Journal of Computer Applications. 108, 3 ( December 2014), 40-43. DOI=10.5120/18895-0183

@article{ 10.5120/18895-0183,
author = { Saurabh Mangal, Aditya Shankar },
title = { Prediction Improvement using Optimal Scaling on Random Forest Models for Highly Categorical Data },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 3 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number3/18895-0183/ },
doi = { 10.5120/18895-0183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:03.882878+05:30
%A Saurabh Mangal
%A Aditya Shankar
%T Prediction Improvement using Optimal Scaling on Random Forest Models for Highly Categorical Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 3
%P 40-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Random Forests are an effective ensemble method which is becoming increasingly popular, particularly for binary classification prediction problems. One of the most popular algorithms for implementing the Random Forest model is the Breiman and Cutler's algorithm and this forms the basis of the "randomForest" package in R. However, a Random Forest model implemented using this package has a limitation, especially in a milieu which has limited computational power, that it cannot handle highly categorical data. In this paper, we present one of the many techniques we tried to improve the performance of a Random Forest Model using highly categorical data. The performance improvement was solely achieved using advanced pre-processing techniques like Optimal Scaling, hence the title of the paper.

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Index Terms

Computer Science
Information Sciences

Keywords

Ensemble Methods Random Forest Prediction with Categorical Variables Optimal Scaling Classification Machine Learning Non-Linear Categorical Prediction.

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