INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Design and Development of Optimal Causal Probability Decision Tree by computing path probability of Internal Causality nodes
Authors Name:
S.SAJIDA
, Dr.K.VijayaLakshmi
Unique Id:
IJSDR2211051
Published In:
Volume 7 Issue 11, November-2022
Abstract:
Data becomes the driving force of the modern world, almost everyone has come across terms like data science, machine learning, artificial intelligence, and data mining. A tree has many real-world analogies, and it turns out that it has influenced a broad area of machine learning, including classification and regression. A decision tree can be used in decision analysis to visually and explicitly represent to make decisions. Though it is a common tool in data mining for developing a strategy to achieve a specific goal, it is also widely used in machine learning, which will be the primary focus in this research paper. Since the trees are generated with a cause and effect relationship, the decision tree's consequence is a Causal probability decision tree. The author proposed a metric for evaluating the Finest Causal Probability Decision Trees by Computing Path Probability of Internal Causality Nodes.
"Design and Development of Optimal Causal Probability Decision Tree by computing path probability of Internal Causality nodes", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 11, page no.316 - 319, November-2022, Available :http://www.ijsdr.org/papers/IJSDR2211051.pdf
Downloads:
000337074
Publication Details:
Published Paper ID: IJSDR2211051
Registration ID:202526
Published In: Volume 7 Issue 11, November-2022
DOI (Digital Object Identifier):
Page No: 316 - 319
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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