Bootstrap Aggregating: Revision history

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30 November 2024

  • curprev 13:5313:53, 30 November 2024‎ Prairie talk contribs‎ 4,396 bytes +4,396‎ Created page with "'''Bootstrap Aggregating''', commonly known as '''Bagging''', is an ensemble learning method designed to improve the stability and accuracy of machine learning models. It works by combining the predictions of multiple base models, each trained on different subsets of the data created through the bootstrap sampling technique. Bagging reduces variance, mitigates overfitting, and improves model robustness. == Overview == Bootstrap aggregating is built on two fundamental co..."
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