COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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They both have the word fitting in them
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Both are errors because of too much simplicity or complexity
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They’re both very common problems in beginner coding
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None of the above
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Detailed explanation-1: -Underfitting means that your model makes accurate, but initially incorrect predictions. In this case, train error is large and val/test error is large too. Overfitting means that your model makes not accurate predictions. In this case, train error is very small and val/test error is large.
Detailed explanation-2: -Any attempt to generalize an already underfitting model will lead to further underfitting since it tends to reduce model complexity. If a model is overfitting, then it is the ideal candidate to apply generalization techniques upon.
Detailed explanation-3: -Underfitting is when the training error is high. Overfitting is when the testing error is high compared to the training error, or the gap between the two is large.
Detailed explanation-4: -both overfitting and underfitting are measured in relative terms, so yes, it is possible to have both at the same time.