Which one of these algorithms 1) reduce variance and 2) either reduce bias or bias does not change: O Random forests O Bagging O Boosting O Bumping O Spline regression

Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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**Question:**
Which one of these algorithms 1) reduces variance and 2) either reduces bias or bias does not change:

- Random forests
- Bagging
- Boosting
- Bumping
- Spline regression

**Explanation:**
This multiple-choice question is designed to assess your understanding of how various machine learning algorithms impact variance and bias in predictive models. When considering different techniques such as Random Forests, Bagging, Boosting, Bumping, and Spline Regression, it's important to evaluate both the reduction of variance and the effect on bias.
Transcribed Image Text:**Question:** Which one of these algorithms 1) reduces variance and 2) either reduces bias or bias does not change: - Random forests - Bagging - Boosting - Bumping - Spline regression **Explanation:** This multiple-choice question is designed to assess your understanding of how various machine learning algorithms impact variance and bias in predictive models. When considering different techniques such as Random Forests, Bagging, Boosting, Bumping, and Spline Regression, it's important to evaluate both the reduction of variance and the effect on bias.
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