COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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No, No
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No, Yes
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Yes, No
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Yes, Yes
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Detailed explanation-1: -Boosting, like bagging, can be used for regression as well as for classification problems. Being mainly focused at reducing bias, the base models that are often considered for boosting are models with low variance but high bias.
Detailed explanation-2: -Bagging avoids overfitting of data and is used for both regression and classification models, specifically for decision tree algorithms.
Detailed explanation-3: -Gradient boosting can be used for regression and classification problems.
Detailed explanation-4: -Bagging is a technique for reducing prediction variance by producing additional data for training from a dataset by combining repetitions with combinations to create multi-sets of the original data. Boosting is an iterative strategy for adjusting an observation’s weight based on the previous classification.
Detailed explanation-5: -In boosting tree individual weak learners are not independent of each other because each tree correct the results of previous tree. Bagging and boosting both can be consider as improving the base learners results.