# why boosting method is sensitive to outliers

I found many articles that state that boosting methods are sensitive to outliers, but no article explaining why.

In my experience outliers are bad for any machine learning algorithm, but why are boosting methods singled out as particularly sensitive?

How would the following algorithms to rank in terms of sensitivity to outliers: boost-tree, random forest, neural network, SVM, and simple regression methods such as logistic regression?

• I've edited to try to clarify (also if you put spaces at the beginning of a line, stackexchange will treat it as code). To your second para, boosting is so what? You might have to define sensitivity. – Jeremy Miles Mar 3 '15 at 23:28
• Also, outliers and noice are not the same thing. – Jeremy Miles Mar 3 '15 at 23:28
• I wouldn't mark this question as resolved yet. It is not clear if boosting actually suffers from outliers more than other methods or not. It seems the accepted answer was accepted mostly because of confirmation bias. – rinspy Aug 17 '17 at 15:42
• Can you share some of these articles, please? – acnalb Dec 1 '18 at 22:34

The algorithms you specified are for classification, so I'm assuming you don't mean outliers in the target variable, but input variable outliers. Boosted Tree methods should be fairly robust to outliers in the input features since the base learners are tree splits. For example, if the split is x > 3 then 5 and 5,000,000 are treated the same. This may or may not be a good thing, but that's a different question.