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"Why would the application of boosting prevent underfitting?"

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

Source:
https://www.cs.cornell.edu/courses/cs4780/2015fa/web/lecturenotes/lecturenote13.html

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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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