COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Bagging reduce variance of the classifier
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Bagging increase the variance of the classifier
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Bagging can help make robust classifier from unstable classifiers
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Bagging results in increased bias
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Detailed explanation-1: -Answer 11: c) Bagging often reduces overfitting is FALSE Bagging is an approach to diminish the variance of your prediction by creating extra data for training from your un…
Detailed explanation-2: -The good thing about Bagging is, that it also does not increase the bias again, which we will motivate in the following section. That is why the effect of using Bagging together with linear regression is low: You can not decrease the bias via Bagging, but with Boosting.
Detailed explanation-3: -1) Which of the following is/are true about bagging trees? Both options are true. In Bagging, each individual trees are independent of each other because they consider different subset of features and samples.
Detailed explanation-4: -Bagging does effect models with high bias, but it reduces its accuracy instead.