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feature importance methods being used?

data-dino
Blue LED

feature importance methods being used?

Which feature importance methods does the data robot platform use? How can I choose between different methods, if there's one I want to use? thanks for helping me

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3 Replies
Baydir
Data Scientist
Data Scientist

Hi @data-dino , the feature importance method we use is called "Alternating Conditional Expectation (ACE)". It is a univariate measure of correlation between the feature and the target. ACE scores detect non-linear relationships, but as they are univariate, they do not detect interaction effects. If you want to go into more details you can have a look to this paper: https://www.jds-online.com/files/JDS-156.pdf

You can't choose a different method. Out of curiosity, which method you have in mind ? 

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Bogdan Tsal-Tsalko
Data Scientist
Data Scientist

Hi! 
Datarobot provides a variety of insights for features that may be addressed as feature importance:

  • Feature importance: ACE score built during EDA (exploratory data analysis) as mentioned above
  • Feature associations: Feature clusters, feature association matrix, feature association pair plots. 
  • Feature Impact: built using a trained model, Permutation based, SHAP-based
  • Feature Effects: provide model detail insights on a per-feature basis
  • Insights: Tree-based, Coefficients-based, Word clouds, etc.

Overall, DataRobot recommends using either permutation-based or SHAP-based Feature Impact as they show results for original features in predictive models.
Hope this answer will help. If you need further explanations - feel free to ask.

Andrew Nicholls
Data Scientist
Data Scientist

Hi @data-dino, I'd like to ask a clarifying question. What are you planning on doing once you look at feature importance? What decision will you make based on what you discover?

 

If I understand that I can help recommend what might be best for you. 

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