We can significantly reduce the number of features in our dataset by leveraging DataRobot's ability to train hundreds of high-quality ML models in a matter of minutes.
Feature Importance Rank Ensembling (FIRE) aggregates the rankings of individual features using Feature Impact from several blueprints on the leaderboard. This approach can provide greater accuracy and robustness over other feature reduction methods.
This accelerator shows how to apply FIRE to your dataset and dramatically reduce the number of features without impacting the performance of the final model.
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