JETIREXPLORE- Search Thousands of research papers



Published in:

Volume 7 Issue 11
November-2020
eISSN: 2349-5162

Unique Identifier

JETIR2011209

Page Number

555-558

Share This Article


Title

Exploring Association between Optimal Depth and Number of Features used in Decision Tree

ISSN

2349-5162

Cite This Article

"Exploring Association between Optimal Depth and Number of Features used in Decision Tree", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.555-558, November-2020, Available :http://www.jetir.org/papers/JETIR2011209.pdf

Abstract

Decision Tree (DT) is a widely used predictive modelling tool that has applications spanning a wide range of areas including business, energy modelling, medicine and remote sensing. Decision Tree is a non-parametric supervised learning method used for both classification and regression tasks. Building optimal decision tree has always been an NP problem. A number of factors affect the accuracy of decision trees and a lot of research has been done in this area. In the present work we attempted to explore a relationship between number of features used for modelling and optimal depth of decision tree. Different datasets were tested and it was observed that there exists some kind of relationship between these two. The work may help reduce the testing time significantly by setting an upper bound on depth value during testing for optimal depth.

Key Words

Decision tree, predictive modelling, optimal depth, accuracy.

Cite This Article

"Exploring Association between Optimal Depth and Number of Features used in Decision Tree", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp555-558, November-2020, Available at : http://www.jetir.org/papers/JETIR2011209.pdf

Publication Details

Published Paper ID: JETIR2011209
Registration ID: 303391
Published In: Volume 7 | Issue 11 | Year November-2020
DOI (Digital Object Identifier):
Page No: 555-558
ISSN Number: 2349-5162

Download Paper

Preview Article

Download Paper




Cite This Article

"Exploring Association between Optimal Depth and Number of Features used in Decision Tree", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp555-558, November-2020, Available at : http://www.jetir.org/papers/JETIR2011209.pdf




Preview This Article


Downlaod

Click here for Article Preview