COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)(i). Models which overfit are more likely to have high bias(ii). Models which overfit are more likely to have low bias(iii). Models which overfit are more likely to have high variance(iv). Models which overfit are more likely to have low variance
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(i) and (ii)
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(ii) and (iii)
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(iii) and (iv)
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None of these
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Explanation:
Detailed explanation-1: -Answer: Underfitted models have high bias. Overfitted models have high variance.
Detailed explanation-2: -Underfitted models have low bias ( false ). Overfitted models have high variance ( true ).
Detailed explanation-3: -A model that exhibits small variance and high bias will underfit the target, while a model with high variance and little bias will overfit the target. A model with high variance may represent the data set accurately but could lead to overfitting to noisy or otherwise unrepresentative training data.
Detailed explanation-4: -Parametric or linear machine learning algorithms often have a high bias but a low variance.
There is 1 question to complete.