How to Calculate Feature Importance With Python

Last Updated on August 20, 2020

Feature importance refers to techniques that assign a score to input features based on how useful they are at predicting a target variable.

There are many types and sources of feature importance scores, although popular examples include statistical correlation scores, coefficients calculated as part of linear models, decision trees, and permutation importance scores.

Feature importance scores play an important role in a predictive modeling project, including providing insight into the data, insight into the model, and the basis for dimensionality reduction and feature selection that can improve the efficiency and effectiveness of a predictive model on the problem.

In this tutorial, you will discover feature importance scores for machine learning in python

After completing this tutorial, you will know:

• The role of feature importance in a predictive modeling problem.
• How to calculate and review feature importance from linear models and decision trees.
• How to calculate and review permutation feature importance scores.

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• Update May/2020: Added example of feature selection using importance.