How does boosting reduce bias in a machine learning model?
- By averaging the predictions of many models
- By focusing on one strong model
- By training only on the easiest examples
- By training sequentially on misclassified examples
Boosting reduces bias by training models sequentially, with each model focusing on the examples that were misclassified by the previous ones. This iterative correction process reduces bias and enhances the overall performance of the model.
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