Sunday, October 2, 2022

AI/ML why boosting method is sensitive to outliers

Outliers can be bad for boosting because boosting builds each tree on previous trees' residuals/errors. Outliers will have much larger residuals than non-outliers, so gradient boosting will focus a disproportionate amount of its attention on those points.


referenceS:

https://stats.stackexchange.com/questions/140215/why-boosting-method-is-sensitive-to-outliers


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