Implementation of ID3 and Naive Bayes Classifiers in MATLAB
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Resource Overview
MATLAB implementation of ID3 decision tree and Naive Bayes classifiers with practical usage examples and algorithmic explanations
Detailed Documentation
MATLAB provides powerful capabilities for implementing both ID3 decision tree and Naive Bayes classifiers. The ID3 algorithm builds decision trees using information gain calculations to select optimal attributes for splitting, while the Naive Bayes classifier applies Bayes' theorem with strong independence assumptions between features. The implementation includes core functions for data preprocessing, model training, and prediction evaluation. You can find practical examples demonstrating how to apply these classifiers to real datasets, complete with visualization of decision boundaries and performance metrics. These examples help users understand the underlying algorithms, parameter tuning techniques, and practical considerations for different types of classification problems. The code structure emphasizes modular design, making it easy to extend or modify specific components for customized applications.
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