Implementing Lasso and Ridge regression

There are ways to limit the influence of coefficients on the regression output. These methods are called regularization methods, and two of the most common regularization methods are Lasso and Ridge regression. We cover how to implement both of these in this recipe.

Getting ready

Lasso and Ridge regression are very similar to regular linear regression, except that we add regularization terms to limit the slopes (or partial slopes) in the formula. There may be multiple reasons for this, but a common one is that we wish to restrict the number of features that have an impact on the dependent variable.

How to do it...

We proceed with the recipe as follows:

We will use the Boston Housing dataset again and set up our functions in the same way as in the previous recipes. In particular we need define_feature_columns_layers, make_input_fn, and create_interactions. We again first load the libraries, and then we define a new create_ridge_linreg...