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Machine Learning
An ensemble method that sequentially improves weak learners.
Detailed Explanation
Boosting is a machine learning ensemble technique that combines multiple weak learners, often simple models like decision stumps, to create a strong overall model. It improves performance by training each new model to focus on the errors of the previous ones, sequentially reducing bias and variance, resulting in higher accuracy and robustness in predictive tasks.
Use Cases
- •Boosting enhances spam detection accuracy by sequentially correcting misclassified emails, resulting in a more reliable filter system.
Related Terms
Other terms in the Machine Learning category