Adaboost: An Iterative Machine Learning Algorithm

Adaboost source code implements an iterative algorithm whose core concept involves training different classifiers (weak learners) on the same training dataset, then combining these weak learners to construct a more powerful final classifier (strong learner). The iterative process of the Adaboost algorithm can be repeated multiple times, with each iteration generating a new weak learner, thereby continuously improving the accuracy and stability of the final classifier. Through iterative training of multiple weak learners and combining them into a strong classifier, the Adaboost algorithm achieves excellent performance in solving binary classification problems. The code implementation typically includes weight initialization for training instances, weak learner selection based on weighted error rates, and weight updates for misclassified samples. Additionally, the Adaboost algorithm demonstrates strong generalization capabilities, making it applicable to various types of datasets. Consequently, the Adaboost algorithm has gained widespread application and research in the machine learning field, with common implementations using decision stumps or shallow trees as weak learners.