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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: November 2022

Volume 7 | Issue 11

Impact factor: 8.15

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Paper Title: 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.
Keywords: Decision Tree, Optimal Probability, Correlation, Causal inference internal node Causality,path probability,path scores
Cite Article: "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: 000150694
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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