Python Machine Learning Cookbook

Computing the relative importance of features

Are all the features equally important? In this case, we used 13 input features, and they all contributed to the model. However, an important question here is, "How do we know which features are more important?" Obviously, all the features don't contribute equally to the output. In case we want to discard some of them later, we need to know which features are less important. We have this functionality available in scikit-learn.

How to do it…

1. Let's plot the relative importance of the features. Add the following lines to housing.py:

   plot_feature_importances(dt_regressor.feature_importances_, 'Decision Tree regressor', housing_data.feature_names) plot_feature_importances(ab_regressor.feature_importances_, ...