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 OCPDT by computing Path Probability by multiplying Probability differences of all Branch wise Internal nodes
Authors Name:
S.SAJIDA
, Dr K.VIJAYALAKSHMI
Unique Id:
IJSDR2211167
Published In:
Volume 7 Issue 11, November-2022
Abstract:
The most powerful and widely used tool for classification and prediction is the Decision Tree. A Decision tree is a tree structure that looks like a flowchart, with each internal node representing a test on an attribute, each branch representing a test outcome, and each leaf node (terminal node) holding a class label. The strengths of Decision Trees are: Decision trees are able to generate understandable rules. Decision trees perform classification without requiring much computation time. But it also suffers some limitations such as: The training of decision trees can be computationally expensive. A decision tree's growth requires extensive computational work. Each candidate splitting field at each node must first be sorted in order to determine which split is best. Some algorithms employ combinations of fields, so it is necessary to look for the best combining weights. Due to the necessity of creating and comparing numerous candidate sub-trees, pruning algorithms can also be costly. 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 Path Probability by multiplying Probability differences of all Branch wise internal nodes.
"Design and Development of OCPDT by computing Path Probability by multiplying Probability differences of all Branch wise Internal nodes ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 11, page no.1112 - 1115, November-2022, Available :http://www.ijsdr.org/papers/IJSDR2211167.pdf
Downloads:
000336256
Publication Details:
Published Paper ID: IJSDR2211167
Registration ID:202752
Published In: Volume 7 Issue 11, November-2022
DOI (Digital Object Identifier):
Page No: 1112 - 1115
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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