MATLAB Implementation of ID3 Algorithm with Decision Tree Visualization

In this article, we explore the implementation of ID3 algorithm and present the results through tree diagram visualization. Decision trees serve as classification tools for data prediction purposes. The implementation begins by constructing a decision tree from training dataset. If the tree cannot correctly classify all instances, specific exceptions are incorporated into the training data, and the process iterates until an accurate decision set is achieved. The core implementation involves calculating entropy using MATLAB's logarithmic functions, determining information gain for attribute selection, and implementing recursive node splitting based on maximum gain criteria. Additionally, various other algorithms and techniques such as random forests and gradient boosting can be employed for decision tree construction and optimization, representing valuable areas for further research. MATLAB functions like fitctree and treeplot may be utilized for efficient implementation and visualization.