2.3.6 Elastic Net Model
Elastic network36 is a linear regression model using L1 and L2 norm as prior regular term training. This combination allows you to learn a non-zero sparse model with a few parameters, like Lasso, but it still retains some regular properties like Ridge.The convex combination of L1 and L2 can be controlled by l1_ratio parameter.Elastic networks are an iterative method.T he best thing about elastic networks is that they can always produce efficient solutions.Because it doesn’t cross paths, the solutions are pretty good.But the most attractive thing about elastic networks is not their efficient solution, but their rate of convergence.Elastic networks are very useful when many features are linked.Lasso is likely to consider only one of these features at random, while elastic networks prefer two.In practice, one advantage of the tradeoff between Lasso and Ridge is that it allows the Ridge stability to be inherited Under rotate.