Boosting

Boosting is a machine learning ensemble meta-algorithm that combines weak learners to form a strong learner. In boosting, each weak learner is trained on a subset of the data, and the algorithm assigns higher weights to the misclassified samples. The next weak learner is then trained on the updated weights, and the process is repeated until the desired level of accuracy is achieved. Boosting is often used in classification problems, and it can improve the performance of decision trees, neural networks, and other machine learning algorithms. AdaBoost and Gradient Boosting are two popular boosting algorithms.
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