• Blog
  • Model Explainability with SHAP in DataRobot

Model Explainability with SHAP in DataRobot

July 7, 2020
· 2 min read

This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about the DataRobot AI Platformdata science, and more.

State-of-the-art machine learning models have a reputation for being accurate but difficult to interpret. DataRobot’s explainable AI features help you understand not just what your model predicts, but how it arrives at its predictions. In this learning session we take a look at SHAP values (Shapley values) for both Feature Impact and Prediction Explanation, which is newly integrated into DataRobot in release 6.1. SHAP is a model-explanation system based on Shapley values, which tells you how much each model feature affects each prediction. A wide variety of top-performing DataRobot blueprints now integrate SHAP, including linear models, trees and tree ensembles, and multistage combinations of these.

No matter how you interact with models, you will get some useful insights from SHAP values. Model developers can learn which features matter, which helps focus their development efforts. Model evaluators and regulators can sanity-check predictions against domain knowledge and business rules. Model consumers can learn which features were most important in individual cases, and use that as a guide for actionable next steps. Regardless of your role, seeing how the model makes its predictions can help you understand and trust it.


  • Mark Romanowsky (DataRobot, Data Scientist—Explainable AI)
  • Rajiv Shah (DataRobot, Data Scientist—Customer Success)
  • Jack Jablonski (DataRobot, AI Success Manager)

More Information

SHAP-based Prediction Explanations
SHAP reference (see the section “Additivity in Prediction Explanations” to learn why sometimes SHAP values do not add up to the prediction).

See DataRobot in Action
Request a demo

About the author
Linda Haviland
Linda Haviland

Community Manager

Meet Linda Haviland
  • Listen to the blog
  • Share this post
    Subscribe to DataRobot Blog
    Newsletter Subscription
    Subscribe to our Blog