AdaBoost (Adaptive Boosting) is a supervised machine learning algorithm used to improve the accuracy of weak classification models. The AdaBoost algorithm iteratively trains a sequence of weak classifiers on different subsets of data, assigning higher weights to data that was misclassified in previous iterations. It then combines the results of these weak classifiers into a weighted strong classifier, with the best performing weak classifiers having a higher weight in the final classification.
The AdaBoost algorithm is known for its ability to significantly improve the accuracy of machine learning models, especially in complex classification tasks with large and noisy datasets. Moreover, it is easy to implement and can be adapted to different types of weak machine learning algorithms, which makes it popular in machine learning practice.
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