Decision Tree Algorithm Implementation

This is a decision tree algorithm implemented in MATLAB. The algorithm provides thorough explanations of each step and functionality. The implementation begins with data preprocessing stages, including data cleaning and feature selection, which may involve handling missing values and selecting relevant features using statistical methods. Next, the algorithm constructs the decision tree model by recursively splitting nodes based on information gain calculations using entropy or Gini impurity metrics to determine optimal splitting criteria. During the tree-building process, the implementation addresses overfitting concerns by incorporating pruning strategies such as reduced-error pruning or cost-complexity pruning to optimize tree size. The final output is a complete decision tree structure that provides interpretable results through clear decision paths and node conditions. This practical algorithm finds applications across various domains including medical diagnosis (symptom classification), financial risk assessment (credit scoring), and market prediction (customer behavior analysis). The implementation includes MATLAB functions for tree visualization, prediction methods, and performance evaluation metrics to facilitate understanding and practical usage of the decision tree methodology.