Beginner · Evaluation
Feature importances
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Feature importances indicate the relative contribution of each feature to a model's predictions, showing which features are most influential.
Technical Definition
Feature importances indicate the relative contribution of each feature to a model's predictions, showing which features are most influential.
How it works
Feature importances, also known as variable importances, are scores assigned to input features by a machine learning model. These scores quantify how much each feature contributed to the model's predictive performance. Features with higher importance values have a greater impact on the model's output.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Feature set — A feature set is the collection of all input features used by a machine learning model during training and inference.
- Model explainability — The ability to understand and interpret how an AI model arrives at its decisions or predictions, making its operations transparent.