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I'm doing a support vector regression with the dependent variable representing measurements from an uncalibrated sensor (measurement error between 2% and 20%) and I want to study the effect of this error on the model performance. What's the best method to inject error on the dependent variable samples ? is it sufficient to do this by adding white noise? Any help or suggestion is appreciated

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Adding white noise is simple enough and should work, alternatively you could permute the values of your variable, which is another commonly used method.

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