COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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What is limitation of under sampling
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It could make model underfit
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It could make model overfit
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Either A or B
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None of the above
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Explanation:
Detailed explanation-1: -Underfitting is a scenario in data science where a data model is unable to capture the relationship between the input and output variables accurately, generating a high error rate on both the training set and unseen data.
Detailed explanation-2: -both overfitting and underfitting are measured in relative terms, so yes, it is possible to have both at the same time.
Detailed explanation-3: -A model that is underfit will have high training and high testing error while an overfit model will have extremely low training error but a high testing error.
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