Elastic net regularization is a method that reduces the danger of overfitting in the context of regression (see http://en.wikipedia.org/wiki/Elastic_net_regularization). The elastic net regularization combines linearly the **least absolute shrinkage and selection operator** (**LASSO**) and **ridge** methods. LASSO limits the so-called L1 norm or Manhattan distance. This norm measures for a points pair the sum of absolute coordinates differences. The ridge method uses a penalty, which is the L1 norm squared. For regression problems, the goodness-of-fit is often determined with the **coefficient of determination** also called
**R squared** (see http://en.wikipedia.org/wiki/Coefficient_of_determination). Unfortunately, there are several ...

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