**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.

Answer 1) reduce variance: we use bagging algorithm because: 1.weight of each classifier is same…

Answered: Which one of these algorithms 1) reduce…

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)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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