In the bias-variance decomposition of the expected test error, which component represents the error due to the noise in the training data?
- Bias
- Both Bias and Variance
- Neither Bias nor Variance
- Variance
In the bias-variance trade-off, the component that represents the error due to noise in the training data is both bias and variance. Bias refers to the error introduced by overly simplistic assumptions in the model, while variance represents the error due to model sensitivity to fluctuations in the training data. Together, they account for the expected test error.
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