MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Ensemble is the art of combining diverse set of learners (individual models) together to improvise on the stability and predictive power of the model. In the above example, the way we combine all the predictions together will be termed as Ensemble Learning.

Detailed explanation-2: -Ensemble methods are techniques that aim at improving the accuracy of results in models by combining multiple models instead of using a single model. The combined models increase the accuracy of the results significantly. This has boosted the popularity of ensemble methods in machine learning.

Detailed explanation-3: -The most common method to combine models is by averaging multiple models, where taking a weighted average improves the accuracy. Bagging, boosting, and concatenation are other methods used to combine deep learning models. Stacked ensemble learning uses different combining techniques to build a model.

Detailed explanation-4: -Voting and averaging are two of the easiest ensemble methods. They are both easy to understand and implement. Voting is used for classification and averaging is used for regression. In both methods, the first step is to create multiple classification/regression models using some training dataset.

Detailed explanation-5: -Hybrid Model: A technique that combines two or more different machine learning models in some way.