"Why would the application of boosting prevent underfitting?"

I read in some paper that applying boosting would prevent you from underfitting. Why is that?



1 Answer 1


It seems to me that the article is approaching it from the perspective of the base classifier. For example if the base classifier is a Decision Tree with a max depth of 1 (or any other severely limiting factors) it will underfit. In general, boosting adds a classifier of the same structure and train it on the data the previous classifier got incorrect which leads to a more general model; hence "less underfitting".


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