Applied Data Science Coding in Python: How to get Feature Importance

In order to understand the importance of different features in a dataset, you can use a technique called “feature importance.” This technique allows you to see which features in your dataset have the biggest impact on the outcome or target variable.

There are many ways to calculate feature importance in Python, but one popular method is called “Random Forest Feature Importance.” This method uses the Random Forest algorithm, which is an ensemble learning method that creates multiple decision trees and averages their results to make a final prediction.

The idea behind Random Forest Feature Importance is that each decision tree in the forest will have different feature importance scores, so by averaging these scores, you can get a more accurate picture of the overall importance of each feature.

Another popular method is using XGBoost, which is a gradient boosting library that is highly efficient and easy to use. It also has a feature_importance_ attribute which is similar to the random forest feature importance.

It’s worth noting that most of the feature importance methods are based on the concept of permutation importances, which is a way to estimate the importance of a feature by randomly shuffling its values, and then see how much the performance drops.

There are also other feature importance methods like permutation importance, SHAP (SHapley Additive exPlanations) values, and more.

In summary, feature importance is a technique that can help you understand which features in your dataset are most important for the outcome or target variable. There are many ways to calculate feature importance in Python, but two popular methods are Random Forest Feature Importance and XGBoost’s feature_importance_ attribute.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Feature Importance.