Feature selection notebooks¶
DataRobot offers end-to-end code examples via Jupyter notebooks that help you find complete examples of common data science and machine learning workflows. Review the notebooks that outline feature selection below.
| Topic | Describes... |
|---|---|
| Feature Importance Rank Ensembling | Learn about the benefits of Feature Importance Rank Ensembling (FIRE)—a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. |
| Advanced feature selection with Python | Use Python to select features by creating aggregated Feature Impact. |
Updated October 11, 2023
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