Question

36. Which among the following are some of the differences between bagging and boosting? 1.
In bagging we use the same classification algorithm for training on each sample of the
data, whereas in boosting, we use different classification algorithms on the different
training data samples. 2. Bagging is easy to parallelise whereas boosting is inherently a
sequential process 3. In bagging we typically use sampling with replacement whereas in
boosting, we typically use weighted sampling techniques. 4. In comparison with the
performance of a base classifier on a particular data set, bagging will generally not
increase the error whereas as boosting may lead to an increase in the error.
O A. 1,2,3
O B. 2,3,4
O C. 1
O D. All the mentioned

Answer 1

Concept: To find the difference between Bagging and Boosting

Given: A few statements and information

Find: The main differences between Bagging and Boosting

Solution: The correct option is (D) All the mentioned

In Bagging we use same classification algorithm for training on each sample of the data, whereas in boosting, we use different classification algorithms on different training data samples.

Bagging is easy to parallelise whereas boosting is inherently a sequential process.

In bagging, we typically use sampling with replacement whereas in boosting, we typically use weighted sampling techniques.

Bagging will not increase error while boosting can lead to an increase in errors.

Hence, we have now found the differences between bagging and boosting.

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Answer 2

Answer:

The answer is option D.

Explanation:

• While in boosting we use multiple classification algorithms on different training data samples, in bagging we utilize the same classification algorithm for training on each sample of the data.
• Boosting is a sequential operation by nature, but bagging is simple to parallelize.
• While we frequently use weighted sampling approaches in boosting, we frequently employ sampling with replacement in bagging.
• While boosting may cause an increase in errors, bagging will not, in comparison with the performance of a base classifier on a particular data set.

Thus the correct answer is Option D.

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