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Could you explain the concept of error in linear regression and its significance in ensuring precise modeling?

Machine Learning Engineer

Palantir Technologies

Google

Blend

Hewlett Packard

trivago

Arm

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Anonymous

8 months ago

An error in linear regression is normally distributed. This is to ensure valid statistical inference of the computed coefficients and for hypothesis testing.  Intuitively, we want to make sure the error is normally distributed to compensate for both underfitting and overfitting

Anonymous

a year ago

Linear regression assumes that the noise is gaussian. This would cause error in the approximation if the noise added was not from a symmetric distribution. For example, if the error was drawn from a poisson distribution, then the tail events would overly influence the fit.

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