MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Which of the following option is / are correct regarding benefits of ensemble model? 1 and 2 are the benefits of ensemble modeling. Option 3 is incorrect because when we ensemble multiple models, we lose interpretability of the models.

Detailed explanation-2: -Noise, Bias and Variance: The combination of decisions from multiple models can help improve the overall performance. Hence, one of the key reasons to use ensemble models is overcoming noise, bias and variance.

Detailed explanation-3: -Ensemble learning techniques have been proven to yield better performance on machine learning problems. We can use these techniques for regression as well as classification problems. The final prediction from these ensembling techniques is obtained by combining results from several base models.

Detailed explanation-4: -The most popular ensemble methods are boosting, bagging, and stacking. Ensemble methods are ideal for regression and classification, where they reduce bias and variance to boost the accuracy of models.