Implementation of the Classic AdaBoost Algorithm

This AdaBoost implementation represents the classical version of the algorithm, making it particularly suitable for beginners exploring machine learning fundamentals. The codebase contains extensive detailed comments that guide readers through each computational step, including weight initialization, weak learner selection, and error calculation procedures. The implementation demonstrates core AdaBoost concepts such as iterative re-weighting of training samples and the combination of weak classifiers into a strong ensemble model. By studying this code, developers can understand how to handle misclassified instances through weight updates and learn the mathematical foundation behind the algorithm's convergence properties. As AdaBoost remains fundamental in machine learning for creating powerful ensemble models, this implementation serves as an excellent educational resource for those interested in mastering boosting techniques and enhancing their model-building capabilities.