Decision tree: basic theory, algorithm formulation and implementation

Tianyi Yu

doi:10.1117/12.2628025

22 April 2022 Decision tree: basic theory, algorithm formulation and implementation

Tianyi Yu

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631F (2022) https://doi.org/10.1117/12.2628025
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Machine learning is a subfield of artificial intelligence that teaches a machine how to learn. It has drawn research interest in many research areas, including computer science, engineering technology, and statistics. It also has growing impacts on our daily life. Our life is gradually being affected by algorithms regardless of realizing it or not. For example, the history of your Internet research is shared with companies. When I searched for “football kits” on google, it suggested 10 or 20 most related links; after I clicked on one of the links, the Internet then recorded this as a piece of data; the computer would learn from it to provide improved suggestions next time. Decision trees are one of the most important machine learning models. It uses a tree-like model of decisions and consequences to help classify experiment sets of data. This article summarises the algorithm of decision trees by investigating its basic theories, algorithms, and implementations.

Citation Download Citation

Tianyi Yu "Decision tree: basic theory, algorithm formulation and implementation", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631F (22 April 2022); https://doi.org/10.1117/12.2628025

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