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AdaBoost, the Adaptive Boosting algorithm, harnesses the power of decision trees 🌲 and combines multiple weak learners into a strong learner by giving more weight to misclassified instances. 💪🔍 Through iterative training and emphasizing difficult cases, AdaBoost creates a robust ensemble model that excels in tackling complex classification tasks. 🌟🎯

AdaBoost, short for Adaptive Boosting, is an algorithm that uses a decision tree (or any other base learner) to combine multiple weak learners into a strong learner. The algorithm works by iteratively training weak learners on different subsets of the data. In each iteration, more weight is given to the misclassified instances from previous iterations, which allows the subsequent weak learners to focus on these difficult cases.

By adjusting the weights of the instances, AdaBoost emphasizes the misclassified samples, forcing subsequent weak learners to pay more attention to them during training. This iterative process continues until a predefined number of weak learners is reached, or a desired level of performance is achieved.

The final strong learner in AdaBoost is created by combining the predictions of all the weak learners, typically through a weighted majority voting scheme. This approach allows AdaBoost to effectively handle complex classification tasks by leveraging the collective strength of multiple weak learners.

Random Forest (A) is an ensemble learning algorithm that combines multiple decision trees through bagging, where each tree is trained on a random subset of the data. K-Means (C) is a clustering algorithm used for unsupervised learning, not ensemble learning. K-Nearest Neighbors (D) is a supervised learning algorithm that classifies instances based on the majority vote of its k nearest neighbors, without utilizing boosting techniques.

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