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Chapter 09 Ensembles by pujithakakani0 is a document available to read on EtoBox.

Ensemble methods in machine learning combine multiple models to enhance prediction accuracy, primarily through Bagging and Boosting techniques. Bagging reduces variance by training models on random subsets of data, while Boosting focuses on correcting errors of previous models to reduce bias. Decision fusion techniques, including fixed and trained rule fusion, further improve model performance by combining outputs from various classifiers.

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pujithakakani0

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EN