<p>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 &quot;less underfitting&quot;.</p>

CC BY-SA 4.0
2021-06-13T21:28:47.923Z
2021-06-13T21:28:47.923Z
A_Arnold
<p>&quot;Why would the application of boosting prevent underfitting?&quot;</p>
<p>I read in some paper that applying boosting would prevent you from underfitting. Why is that?</p>
<p>Source:<br />
<a href="https://www.cs.cornell.edu/courses/cs4780/2015fa/web/lecturenotes/lecturenote13.html" rel="nofollow noreferrer">https://www.cs.cornell.edu/courses/cs4780/2015fa/web/lecturenotes/lecturenote13.html</a></p>

<machine-learning><underfitting><boosting>
Why would the application of boosting prevent underfitting?
jennifer ruurs
# Why would the application of boosting prevent underfitting?

(Asked by: username_0 on 2019-11-14)

"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>

**Tags:** machine-learning, underfitting, boosting


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## 1 answers

### Answer by username_1 on 2021-06-13. Score: 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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