APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of the following is / are true about weak learners used in ensemble model?
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They have low variance and they don’t usually overfit
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They have high bias, so they can not solve hard learning problems
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They have high variance and they don’t usually overfit
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None of these
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Explanation:
Detailed explanation-1: -Which of the following is / are true about weak learners used in ensemble model? Weak learners are sure about particular part of a problem. So they usually don’t overfit which means that weak learners have low variance and high bias.
Detailed explanation-2: -These weak learners will often be computationally simple as well. Typically, the reason base models don’t perform very well by themselves is because they either have a high bias or too much variance, which makes them weak.
Detailed explanation-3: -Bagging consists in fitting several base models on different bootstrap samples and build an ensemble model that “average” the results of these weak learners.
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