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 definitions of R squared. Also, the name is a bit misleading, since negative values are possible. A perfect fit would have a coefficient of determination of one. Since the definitions allow for a wide range of acceptable values, we should aim for a...