Boosting Algorithms
Boosting Algorithms are a set of machine learning algorithms that combine weak learners to form a strong learner. The idea behind boosting is to iteratively train a sequence of weak models on different subsets of the data, and then combine them to form a single strong model. Each weak model is trained on a subset of the data, and the data points that are misclassified by the previous model are given more weight in the subsequent model. This process continues until a stopping criterion is met, such as a maximum number of iterations or a minimum error rate. Boosting algorithms are often used in classification problems, such as spam filtering, image recognition, and fraud detection.
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