Decision tree model in supervised learning
YANGGE MENG · 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022) · 2022
Machine learning has become a hot topics in the past few years. The challenge of generic classification is akin to the problem of medical diagnosis. On a case or an object, measurements are taken. We then wish to guess which class the case belongs to based on these measurements. Machine learning may appear to ordinary people to be too advanced; nonetheless, it is used in almost every aspect of our life. One of the most significant machine learning models is the decision tree. To classify trial sets of data, it employs a tree-like model of decisions and outcomes. We investigate the basic theories, techniques, and applications of decision trees in this article.