Explanation Clustering

Community Team
Community Team
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(Part of a model building learning session series.)

During this learning session, Customer Success Data Scientist Rajiv Shah, Public Sector GM & Chief Data Scientist Chandler McCann , and Customer Facing Data Scientist Andrew Mathis discuss advanced techniques for using Prediction Explanations in DataRobot.

After demonstrating how to generate these explanations from DataRobot models, the pair focus on explanation clustering, a technique which has proven very useful for providing “supervised clustering” insights. DataRobot customers have been using this technique for several years and now, with this learning session, we are sharing our recommended approach for explanation clustering to the broader public.


  • Chandler McCann (DataRobot, GM & Chief Data Scientist )
  • Rajiv Shah (DataRobot, Customer Facing Data Scientist)
  • Andrew Mathis (DataRobot, Customer Facing Data Scientist)

Now what?

After watching the learning session, you should check out these resources for more information.


We look forward to hearing from you!

If you have comments or questions that weren't answered in the learning session, or just want to chat more with Chandler, Rajiv, and Andrew, you can click Comment (below) and post them now, or send email to learning_sessions@datarobot.com. 

1 Comment
Data Scientist
Data Scientist

For those looking for the accompanying code - you can visit our Github to get implementations: 

R: https://github.com/datarobot-community/pe-clustering-R
Python: https://github.com/datarobot-community/pe-clustering-python 

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