# Model Explainer

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark> The **Model Explainer** functionality has been provided for the Data Science Lab models and Auto ML models.*

*The user can click on the below given links to get redirected to the respective pages.*

1. *The **Model Explainer** functionality for **Auto ML Models***
2. *The* [***Model Explainer***](https://docs.bdb.ai/data-science-lab/~/changes/lSgdXe3Do34tLrgwmc0c/project/tabs-for-a-data-science-lab-project/tabs-for-tensorflow-and-pytorch-environment/notebook/notebook-page/notebook-operations/models/model-explainer) *functionality for **Data Science Models***
   {% endhint %}

* Navigate to the ***Model*** tab.
* Select an Auto ML model from the displayed list.
* Click the ***Expand for Model***&#x73; option.
* Click the ***Model Explainer*** option.

<figure><img src="https://1025450693-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Figrg2b2URgwMO5YmejDu%2Fuploads%2FCOEVToVxITiahaRGmlxJ%2Fimage.png?alt=media&#x26;token=ada8be90-21fc-4ae4-a722-c5b04d2da2f0" alt=""><figcaption></figcaption></figure>

* The ***Model Explainer*** page opens explaining the selected **Auto ML** model.
* &#x20;This page displays the various tabs to explain the Auto ML model.
* The ***Feature Importance*** tab opens by default.

<figure><img src="https://1025450693-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Figrg2b2URgwMO5YmejDu%2Fuploads%2FcaD30LgOkib7v9yzWCnR%2Fimage.png?alt=media&#x26;token=8222157e-e132-4045-b8a0-4054a9a4973c" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark>*&#x20;

* *The Model Explainer dashboard will display the charts based on the selected Algorithm types (Classification and Regression).*
* *Other than Classification Stats and Regression Stats the other tabs display similar plots for both Classification and Regression models.*

{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
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```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
