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Bootstrap Aggregation (Bagging)
An ensemble machine learning technique that improves the stability and accuracy of algorithms by combining multiple models built from random samples of the data.
Implications
A machine learning ensemble technique that improves model accuracy by training multiple models on random subsets of the data and aggregating their predictions.
Example
Example: A data scientist uses bagging to improve the accuracy of a predictive model for customer churn, combining the outputs of multiple decision trees.
Related Terms
Different from boosting, which focuses on correcting errors of previous models, bagging reduces variance by averaging multiple models trained on different data subsets.
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