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8. Model Explainability
The Arize platform can help you understand why your model produced its predictions.
To set up feature importance values for your model, visit here to learn how to set it up.

Analyzing Feature Importance Values

Global Feature Importance

across all your predictions, what was the most impt features
By default, the model Explainability tab will show the global feature importance values across all predictions within the specified time range.

Cohort Feature Importance

The dropdown filters at the top of the page allow you to understand the importance of your model's features across a cohort or subset of your predictions.
Select a cohort of predictions using the model version, feature and prediction label filters:

Local Feature Importance

If you need per-prediction explainability: The ability to get an explanation for a single prediction based on a prediction ID lookup -- please reach out to your Arize support team for examples on enabling per-prediction visibility into your account.

Using Explainability to Troubleshoot Drift

On the model's Drift tab, sort feature drift by Prediction Drift Impact and Feature Importance.

Using Explainability to Troubleshoot Performance

On the model's performance tab, sort performance breakdown by Feature Importance.

Additional Resources

Last modified 20h ago