What is Gradient Boosting, and how does it work?
- Gradient Boosting always uses a Random Forest
- Gradient Boosting builds trees sequentially, correcting errors using gradients
- Gradient Boosting is a bagging method
- Gradient Boosting reduces model complexity
Gradient Boosting is a boosting method that builds decision trees sequentially. Each tree tries to correct the errors of the previous one by using gradients (direction of the steepest ascent) to minimize the loss function. This leads to a powerful model with improved accuracy.
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