Answer / Pooja Mishra

Boosting is an ensemble learning technique that trains multiple weak learners sequentially where each new model is trained to correct the errors made by the previous models. The key idea behind boosting is to iteratively train models on weighted versions of the training data, focusing more on misclassified instances in earlier rounds.

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