GradientBoosting.md (592B)
1 2 ML D5 3 4 **Definition:** Gradient boosting sequentially adds predictors to an ensemble and fits subsequent models not by instance weights like adaboosting but by residual errors. 5 6 Residual errors are simply the difference between expected and predicted values. As such, gradient boosting does not use weighting in the same way as adaboosting thus distinguishing the two. It basically tries to predict the error amounts from the prior model and output what it thinks they will be. 7 8 Gradient boosting generally uses stronger learners than adaboosting as this works better with the architecture.