How to do it…

Let's see how to build a ridge regressor in Python:

1. You can use the data already used in the previous example: Building a linear regressor (VehiclesItaly.txt). This file contains two values in each line. The first value is the explanatory variable, and the second is the response variable.
2. Add the following lines to regressor.py. Let's initialize a ridge regressor with some parameters:

from sklearn import linear_model
ridge_regressor = linear_model.Ridge(alpha=0.01, fit_intercept=True, max_iter=10000)

1. The alpha parameter controls the complexity. As alpha gets closer to 0, the ridge regressor tends to become more like a linear regressor with ordinary least squares. So, if you want to make it robust against outliers, you need to assign a higher value to alpha. We considered a value of 0.01, which is moderate.
2. Let's train this regressor, as follows:

ridge_regressor.fit(X_train, y_train)
y_test_pred_ridge = ridge_regressor.predict(X_test)
print( "Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_ridge), 2))
print( "Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred_ridge), 2))
print( "Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred_ridge), 2))
print( "Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred_ridge), 2))
print( "R2 score =", round(sm.r2_score(y_test, y_test_pred_ridge), 2))

Run this code to view the error metrics. You can build a linear regressor to compare and contrast the results on the same data to see the effect of introducing regularization into the model.