What is the difference between Gradient Boosting and AdaBoost?
- Both are the same
- Both focus on increasing bias
- Gradient Boosting is for regression, AdaBoost is for classification
- Gradient Boosting uses gradients to correct errors, while AdaBoost focuses on weighting misclassified instances
Gradient Boosting builds models sequentially, using gradients to correct errors and minimize the loss function. AdaBoost, on the other hand, adjusts the weights of misclassified instances to focus the next model on them. Both aim to improve performance but use different approaches.
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