AdaBoost

Adaptive Boosting, or AdaBoost, combines a sequence of weak learners into a stronger predictor. A weak learner need only perform better than a baseline under the current weighting scheme; decision stumps, trees with a single split, are a common teaching example. Unlike methods that train every learner independently and average afterward, AdaBoost adapts each round to earlier mistakes. In binary classification, training examples begin with weights, often equal. A weak learner is fitted using those weights. Its weighted error determines how much influence it receives in the ensemble: a learner with lower error earns a larger vote, provided it performs better than chance under the algorithm's conditions. The example weights are then adjusted so misclassified examples receive relatively more attention in the next round. The process repeats for a chosen number of rounds, and the final prediction aggregates learner votes. A simple intuition is a series of stumps. The first stump may separate most examples by one feature threshold but miss a cluster. AdaBoost increases the relative weight of those misses; the next stump is then encouraged to address them. Later stumps can correct residual errors, while earlier learners remain in the final weighted combination. This focus can help when mistakes reflect genuine structure that later learners can capture. It can also be a weakness. Incorrect labels, extreme outliers, or examples from a different population may repeatedly receive high weight, drawing attention away from the broader pattern. Inspect difficult examples and evaluate on representative held-out data. More rounds do not guarantee better generalization. AdaBoost is distinct from gradient boosting in its formulation, though both add learners sequentially. Implementations vary in supported losses, estimators, and interfaces. Explain the specific algorithm and library behavior when those details matter. Tune learner complexity and boosting rounds with validation, and compare against simpler baselines rather than assuming a weak learner ensemble must win.