AWS Certified AI Practitioner · Free Practice Question Medium
Question 6
Is it possible to increase both the bias and variance of a machine learning model simultaneously?
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A
No, increasing bias always decreases variance and vice versa, so they cannot be increased at the same time
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B
Yes, increasing both bias and variance simultaneously will improve the model's accuracy and generalization capabilities
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C
Yes, it is possible to increase both bias and variance, but this typically leads to a model that performs poorly due to both underfitting and overfitting
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D
No, it is not possible to increase both bias and variance simultaneously, as they are inversely related
Reveal correct answer
Correct answer: C
Explanation
Correct option:
Yes, it is possible to increase both bias and variance, but this typically leads to a model that performs poorly due to both underfitting and overfitting
Bias is an error introduced by approximating a real-world problem (which may be complex) by a simpler model. High bias can cause the model to miss relevant relations between features and target outputs (underfitting).
Variance is an error introduced by the model's sensitivity to small fluctuations in the training data. High variance can cause the model to mimic the random noise in the training data rather than the intended outputs (overfitting).
It is possible to increase both bias and variance, but doing so usually results in a model that is both underfit and overfit, leading to poor performance. High bias causes the model to miss important patterns (underfitting), while high variance makes the model too sensitive to noise in the training data (overfitting).
For example, if you reduce the amount of training data, the model has less information to learn from. This can increase bias because the model may not capture the underlying patterns well (underfitting). At the same time, with less data, the model can become more sensitive to fluctuations in the training data, increasing variance.
Incorrect options:
Yes, increasing both bias and variance simultaneously will improve the model's accuracy and generalization capabilities - Increasing both bias and variance will not improve the model's performance. High bias can lead to underfitting, and high variance can lead to overfitting, making the model perform poorly.
No, it is not possible to increase both bias and variance simultaneously, as they are inversely related - Bias and variance are not inversely related to the extent that one cannot increase both. It is possible to design a model that suffers from both high bias and high variance.
No, increasing bias always decreases variance and vice versa, so they cannot be increased at the same time - While there is often a trade-off between bias and variance, they can both be high in a poorly designed model.
References:
https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/mlper-09.html
Discussion
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