Permutation Feature Importance

Permutation feature importance is a technique used to assess the importance of features in an ML model. By permuting the values of a feature and measuring the resulting decrease in the model’s performance, we can determine the relative importance of each feature. This lesson introduces the concept of permutation feature importance, explaining how it can be used to gain insights into a model’s feature importance.

Understanding permutation feature importance

Permutation feature importance measures the impact of shuffling or permuting the values of a specific feature on the model’s performance. It quantifies the decrease in the model’s performance when the values of a feature are randomly permuted, indicating the importance of that feature.

Permutation feature importance offers several advantages:

• Model interpretation: It provides insights into the relative importance of features, helping to understand which features have the most influence on the model’s predictions.

• Feature selection: It aids in selecting the most relevant features for a model, allowing for the creation of more efficient and interpretable models.

• Model debugging: It helps identify potential issues, such as overfitting or leakage, as a feature with high importance, despite being permuted, could indicate a ...