Boosting Techniques

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Boosting is an ensembling method that improves performance by training models one after another, with each new model trying to fix the mistakes made so far. The process starts with a simple model trained on the full dataset. After that, the training focus shifts toward the examples the current ensemble handles poorly. Each new model adds a corrective layer, and the final prediction comes from combining all models in the sequence.

A well-known example is AdaBoost, which increases attention on the harder cases as training progresses. Boosting sees wide use with structured data, where it often delivers strong results. Sensitivity to noisy data remains a known risk, and careful tuning helps control overfitting and keeps performance stable.

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