Implementing the AdaBoost Algorithm From Scratch

AdaBoost (Adaptive Boosting) is an ensemble learning technique used for classification and regression tasks. It iteratively trains weak classifiers, with each classifier focusing more on misclassified data points. The final AdaBoost model combines all weak classifiers, assigning higher weight to models with better accuracy. The algorithm updates weights of data points based on classifier performance and normalizes them for each iteration. This iterative process results in a strong classifier through weighted contributions from individual models.

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