Machine Learning MCQ - Differences between bagging and boosting

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

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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 Difference Bagging 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).

 

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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

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