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Can boosting lead to overfitting?
All machine learning algorithms, boosting included, can overfit. Of course, standard multivariate linear regression is guaranteed to overfit due to Stein’s phenomena. If you care about overfitting and want to combat this, you need to make sure and “regularize” any algorithm that you apply.
What is gradient boosting regression?
Gradient boosting is a machine learning technique for regression, classification and other tasks, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Is it possible to overfit a boosting technique?
The overfitting of boosting techniques is a topic that is not yet theoretically understood, but empirically results show that boosting seems to be very robust against overfitting. The usual explanation for this phenomena is as follow: the samples that are incorrectly predicted in one iteration will have higher weight in the next one.
What happens when the number of gradient boosting iterations is too high?
A larger number of gradient boosting iterations reduces training set errors. Raising the number of gradients boosting iterations too high increases overfitting. Monitoring the error of prediction from a distinct validation data set can help choose the optimal value for the number of gradients boosting iterations.
How many base learners are added per boosting iteration?
One base learner will be added per boosting iteration/round and that is probably what people are referring to when talking about iterations. One natural regularization parameter is the number of gradient boosting iterations M (i.e. the number of trees in the model when the base learner is a decision tree).
When to use gradient boosting as regularization parameter?
In addition to using the number of gradients boosting iterations as a regularization parameter, one can use the depth of trees as an efficient regularization parameter. When the depth of trees increases, the model is likely going to overfit the training data.