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Interesting question. First of all, classic linear regression was developed for applications where the scatter is normally distributed. If you plot the residual distribution it should have the classic bell shape. When your data adhere to the these model prerequisites, your can just as well use linear regression. The confidence bounds for linear regression ...


2

If you continue to achieve zero error on new input data with the same original model/algorithm it means the phenomenon you're modeling is deterministic.


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As you write, either approach is feasible. I don't think you can give a general recommendation. In some situations, there may be more existing knowledge pertaining to one than to the other approach - for instance, if you are forecasting a time series, there is much more work on forecasting a continuous target variable (approach 1) than a binary one (approach ...


1

See Uniqueness of the SVM Solution by Burges and Crisp for most of the answers. Regarding "accuracy of the solution" - a couple of notes: in the real world we always find an approximate solution, in other words from a purely numerical stand-point we're always within some $\epsilon$ of the training performance of the optimum. Note that in terms of ...


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