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Random forest

Last updated Oct 27, 2022 Edit Source

Bagging and random forests are “bagging” algorithms that aim to reduce the complexity of models that overfit the training data. In contrast, boosting is an approach to increase the complexity of models that suffer from high bias, that is, models that underfit the training data

See AI/Supervised Learning/Ensemble learning

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