Technical GlossaryMachine Learning
Bagging
An ensemble approach that improves stability by training multiple models on bootstrap samples and combining their outputs.
Bagging is a powerful ensemble principle used to reduce model variance. The same algorithm is trained on different bootstrap samples, and predictions are then combined. This is especially effective for high-variance learners such as decision trees. Its main advantage is that it reduces the random sensitivity of a single model and yields more reliable predictions.
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