Incremental decision tree

Statistical classification

Tree (set theory)

Machine learning

Data mining

Mathematical analysis

Pattern recognition (psychology)

Artificial intelligence

Political science

ID3 algorithm

Representation (politics)

Mathematics

Computer science

Decision tree learning

Decision tree

Politics

Journal of Applied Science and Technology Trends

Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

Classification Based on Decision Tree Algorithm for Machine Learning