In linear least squares we are trying to fit a line to data. A line is the simplest model that can be fit to the data. How is it possible for a linear model to over-fit the data? In short why do we need regularization for fitting a line to a data?
Why do we need regularization for linear least sqaures given that a line is the simplest model possible?
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