Boosting is an ensemble learning technique that converts weak learners into a strong learner by iteratively training models and focusing on errors�??. In a boosting process, models (often simple decision stumps or trees) are trained sequentially: each new model is trained on the data with an emphasis (higher weights) on instances that previous models misclassified. By doing so, each model "boosts"ťthe performance of the ensemble by correcting the mistakes of its predecessors. The final strong classifier is a weighted vote (or sum) of all the weak models. AdaBoost (Freund & Schapire, 1997) is a seminal boosting algorithm that demonstrated this idea by combining many weak decision stumps into a powerful classifier. Boosting tends to reduce bias and can achieve high accuracy, though it may be susceptible to overfitting if taken to extremes. Variants like Gradient Boosting further generalize the concept by fitting new models to the residual errors of the current ensemble. Overall, boosting algorithms (e.g., AdaBoost, Gradient Boosting Machines, XGBoost) are state-of-the-art methods for structured data, known for their ability to model complex relationships by ensembling many simple rules�??.