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On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

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