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.
|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.|