DECISION TREES NEST POSITION TRAINING TRAINING ABOVE TREE DECIDED DETERMINED, REGULATION SWITCH MAXIMIZE ENTROPY

DECISION TREES NEST POSITION TRAINING TRAINING ABOVE TREE DECIDED DETERMINED, REGULATION SWITCH MAXIMIZE ENTROPY

Registration ID: IJNRD_225636

Published ID: IJNRD2407406

DOI: Click Here to Get

Tran Van Anh , Ly Hai Son , Nguyen Thu Nguyet MInh

Decision Tree, Maximize entropy, Machine learning.

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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"DECISION TREES NEST POSITION TRAINING TRAINING ABOVE TREE DECIDED DETERMINED, REGULATION SWITCH MAXIMIZE ENTROPY", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.f54-f61, July-2024, Available :https://ijnrd.org/papers/IJNRD2407406.pdf

Volume 9 Issue 7, July-2024

Pages : f54-f61

Paper Reg. ID: IJNRD_225636

Published Paper Id: IJNRD2407406

Downloads: 000121161

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

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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