Adding normalize and FitIntercept parameter to Train() method in Linear Regression #4
Description
If normalize is true, the regressors X will be normalized before regression. This parameter is ignored when FitIntercept is set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if people wish to standardize, please use preprocessing(which has not be implemented yet). StandardScaler before calling fit on an estimator with normalize=false.
If FitIntercept set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).