Paper Title
DECISION TREES NEST POSITION TRAINING TRAINING ABOVE TREE DECIDED DETERMINED, REGULATION SWITCH MAXIMIZE ENTROPY
Article Identifiers
Authors
Tran Van Anh , Ly Hai Son , Nguyen Thu Nguyet MInh
Keywords
Decision Tree, Maximize entropy, Machine learning.
Abstract
Decision trees are a very popular and effective data classification method. Because building a classification tree does not require parameters, decision trees can work effectively with multi-dimensional data, the results are easy to understand and interpret, the learning and classification steps are fast and accurate. good accuracy, so it has been voted by the machine learning community as the most used and most successful data mining method compared to other methods for many years in a row. Besides, the decision tree learning algorithm is successfully applied in many data mining fields such as: data analysis, text data classification, financial analysis, genetic data classification and regression.
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How To Cite (APA)
Tran Van Anh, Ly Hai Son, & Nguyen Thu Nguyet MInh (July-2024). DECISION TREES NEST POSITION TRAINING TRAINING ABOVE TREE DECIDED DETERMINED, REGULATION SWITCH MAXIMIZE ENTROPY. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), f54-f61. https://ijnrd.org/papers/IJNRD2407406.pdf
Issue
Volume 9 Issue 7, July-2024
Pages : f54-f61
Other Publication Details
Paper Reg. ID: IJNRD_225636
Published Paper Id: IJNRD2407406
Downloads: 000121977
Research Area: Science & Technology
Country: Ward 13, Binh Thanh District, main gate, Viet Nam
Published Paper PDF: https://ijnrd.org/papers/IJNRD2407406.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407406
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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