> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Explainability

> The Arize platform can help you understand why your model produced its predictions.

## Sending Feature Importance

Arize supports 2 methods for ingesting and visualizing feature importance

<CardGroup>
  <Card title="Method" img="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/f430f18f-image.jpeg" href="/ax/machine-learning/machine-learning/how-to-ml/explainability/shap">
    User Calculated SHAP
  </Card>

  <Card title="Method" img="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/171e0488-image.jpeg" href="/ax/machine-learning/machine-learning/how-to-ml/explainability/surrogate-model">
    Surrogate Model
  </Card>
</CardGroup>

<Frame caption="">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/439f78f0-image.jpeg" />
</Frame>

### Additional Resources

* Blog: [What Are Global, Cohort and Local Model Explainability?](https://arize.com/blog/model-explainability-primer/)

* Blog: [Overcoming AI's Transparency Paradox](https://arize.com/blog/ai-transparency/)

<Info>
  Questions? Email us at [support@arize.com](mailto::support@arize.com) or [Slack us](https://arize-ai.slack.com/) in the #arize-support channel
</Info>