Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-13T21:28:47.923Z
Answers.LastActivityDate: 2021-06-13T21:28:47.923Z
Answers.UserDisplayName: A_Arnold
Body: <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>
  
Tags: <machine-learning><underfitting><boosting>
Title: Why would the application of boosting prevent underfitting?
UserDisplayName: jennifer ruurs
ThreadText: # 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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