MATLAB Implementation of Decision Tree Algorithm with Code Examples

Decision tree is a widely used machine learning classification algorithm that partitions data through a tree-like structure. Each internal node represents a decision condition based on feature attributes, branches represent decision outcomes, and final leaf nodes represent classification or prediction results. MATLAB provides comprehensive tools for implementing decision tree algorithms, making it particularly suitable for beginners to quickly get started with machine learning concepts.

In MATLAB, the `fitctree` function serves as the primary method for constructing decision tree classification models. This function supports multiple adjustable parameters, including maximum depth (MaxNumSplits), minimum leaf node observations (MinLeafSize), and split criterion (SplitCriterion), allowing customization for different dataset characteristics. The implementation typically follows: tree = fitctree(X, Y, 'ParameterName', value) where X represents feature data and Y contains class labels. After training, the `predict` function enables classification of new data: Y_pred = predict(tree, X_new), while the `view` function generates visual representation of the tree structure: view(tree, 'Mode', 'graph') for better understanding of the model's decision-making process.

The key advantage of decision trees lies in their intuitive interpretability and clear classification rules, though careful attention must be paid to potential overfitting issues. MATLAB addresses this through built-in pruning functionality via the `prune` method: pruned_tree = prune(tree, 'Level', level_value) which optimizes model complexity by removing unnecessary branches. For beginners, it's recommended to start with default parameters and gradually adjust settings while monitoring performance metrics like accuracy and cross-validation results to understand parameter impacts.

Implementing decision trees in MATLAB provides a solid foundation for understanding fundamental machine learning workflows, including data preprocessing, model training, validation, and visualization - essential skills for progressing to more complex algorithms like random forests or gradient boosting machines.