Design and Development of OCPDT by computing Path Probability by multiplying Probability differences of all Branch wise Internal nodes
S.SAJIDA
, Dr K.VIJAYALAKSHMI
Decision Tree, Optimal Probability, Correlation, Causal inference internal node Causality branch probability, path scores.
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 ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.1112 - 1115, November-2022, Available :https://ijsdr.org/papers/IJSDR2211167.pdf
Volume 7
Issue 11,
November-2022
Pages : 1112 - 1115
Paper Reg. ID: IJSDR_202752
Published Paper Id: IJSDR2211167
Downloads: 000347194
Research Area: Computer Science & Technology
Country: tirupati, andhra pradesh, india
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
