Can you explain the main concept behind boosting algorithms?
- Boosting always uses Random Forest
- Boosting combines models sequentially, giving more weight to misclassified instances
- Boosting focuses on the strongest predictions
- Boosting involves reducing model complexity
Boosting is an ensemble method where models are combined sequentially, with each model focusing more on the instances that were misclassified by the previous models. This iterative process helps in correcting the mistakes of earlier models, leading to improved performance.
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