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Unlike the parallel bagging of random forests, GB's serial additive structure makes it more sensitive to hard-to-fit samples, and it often achieves high accuracy on tabular data, but it trains slowly and is sensitive to hyperparameters (learning rate, tree depth, number of iterations), requiring cross-validation tuning to avoid overfitting.
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11/3/2026
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