(Part of a model building learning session series.)
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)
If you’re a licensed DataRobot customer, search the in-app Platform Documentation for SHAP-based Prediction Explanations and SHAP reference . Also, within the SHAP reference topic see the section "Additivity in Prediction Explanations" to learn why sometimes SHAP values do not add up to the prediction.