MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -This technique is effective on models which tend to overfit on the dataset (high variance models). Bagging reduces the variance without making the predictions biased. This technique acts as a base to many ensemble techniques so understanding the intuition behind it is crucial.

Detailed explanation-2: -Bagging, also known as bootstrap aggregation, is the ensemble learning method that is commonly used to reduce variance within a noisy dataset. In bagging, a random sample of data in a training set is selected with replacement-meaning that the individual data points can be chosen more than once.

Detailed explanation-3: -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-4: -Reduce Variance Using an Ensemble of Models. A solution to the high variance of neural networks is to train multiple models and combine their predictions. The idea is to combine the predictions from multiple good but different models. A good model has skill, meaning that its predictions are better than random chance.