Wednesday, February 22, 2023

Machine Learning MCQ - Differences between bagging and boosting

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, ensemble learning, bagging, boosting, differences between bagging and boosting, bagging vs boosting

Machine Learning MCQ - Differences between ensemble learning methods - bagging and boosting

< Previous                      

Next >


1. Which among the following are some of the differences between bagging and boosting?

a) 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

b) Bagging is easy to parallelize whereas boosting is inherently a sequential process

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

d) 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


Answer: (b), (c), and (d)


(b) Bagging (Bootstrap Aggregation) is an ensemble learning method which trains multiple models independently in parallel. Boosting is an ensemble learning method which trains each new model such that it focuses on correcting the errors made by the previous model.

(c) In the case of Bagging, any element has the same probability to appear in a new data set. Training data subsets are drawn randomly with a replacement for the training dataset. However, for Boosting, the observations are weighted. In Boosting algorithms each classifier is trained on data, taking into account the previous classifiers’ success. Hence, every new training subset comprises the elements that were misclassified by previous models. Misclassified data increases its weights to emphasize the most difficult cases.

(d) Boosting can result in an increase in error over a base classifier due to over-emphasis on existing noise data points in later iterations.


Other differences between bagging and boosting




Base classifiers training

They are trained in parallel

They are trained in sequential manner.

Bias and variance

Decreases model’s variance

Decreases model’s bias

Overfitting problem

Solves the problem

Increases the problem

Weights of the model

Models receive equal weights

Models are weighed according to their performance.

Model building

Each model built independently

Models are influenced by the performance of the previous models.

When to apply

If the classifier shows high variance (unstable).

If the classifier shows high bias (stable).




< Previous                      

Next >




Related links:

What is ensemble learning?

Difference between bagging and boosting ensemble learning techniques in ML

What is bagging?

What is boosting?

Boosting vs bagging

When to use boosting and when to use bagging?

Which is best - bagging or boosting? 

bagging helps in decreasing variance of a model, boosting helps to decrease the bias of a model

bagging works in parallel whereas boosting works in sequential manner

Machine learning solved mcq, machine learning solved mcq

No comments:

Post a Comment

Featured Content

Multiple choice questions in Natural Language Processing Home

MCQ in Natural Language Processing, Quiz questions with answers in NLP, Top interview questions in NLP with answers Multiple Choice Que...

All time most popular contents

data recovery