Theory of decision tree models in classification problems

Yuanzheng Wang; Jingqi Zhang; Linfeng Zhang

doi:10.1117/12.2628046

22 April 2022 Theory of decision tree models in classification problems

Yuanzheng Wang, Jingqi Zhang, Linfeng Zhang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631R (2022) https://doi.org/10.1117/12.2628046
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Nowadays, machine learning has become one of the most popular subjects. The general classification problem is similar to the medical diagnosis problem. Measurements are made on some case or object. Based on these measurements, we then want to predict which class the case is in. Someone may think that machine learning sounds too advanced to normal people; however, machine learning is applied everywhere in our lives. A decision tree is one of the important machine learning models. It uses a tree-like model of decisions and consequences to help classify experiment sets of data. In this article, we summarize the algorithm of decision trees by investigating its basic theories, algorithms, and implementations.

Citation Download Citation

Yuanzheng Wang, Jingqi Zhang, and Linfeng Zhang "Theory of decision tree models in classification problems", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631R (22 April 2022); https://doi.org/10.1117/12.2628046

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