# View model details


Autopilot generates details about the candidate models that you can obtain. These details include the following:
+ A plot of the aggregated SHAP values that indicate the importance of each feature. This helps explain your models predictions.
+ The summary statistics for various training and validation metrics, including the objective metric.
+ A list of the hyperparameters used to train and tune the model.

To view model details after running an Autopilot job, follow these steps:

1. Choose the **Home** icon (![\[Black square icon representing a placeholder or empty image.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/icons/house.png)) from the left navigation pane to view the top-level **Amazon SageMaker Studio Classic** navigation menu.

1. Select the **AutoML** card from the main working area. This opens a new **Autopilot** tab.

1. In the **Name** section, select the Autopilot job that has the details that you want to examine. This opens a new **Autopilot job** tab.

1. The **Autopilot job** panel lists the metric values including the **Objective** metric for each model under **Model name**. The **Best model** is listed at the top of the list under **Model name** and is also highlighted in the **Models** tab.

   1. To review model details, select the model that you are interested in and select **View model details**. This opens a new **Model Details** tab.

1. The **Model Details** tab is divided into four subsections.

   1. The top of the **Explainability** tab contains a plot of aggregated SHAP values that indicate the importance of each feature. Following that are the metrics and hyperparameter values for this model. 

   1. The **Performance** tab contains metrics statistics a confusion matrix. 

   1. The **Artifacts** tab contains information about model inputs, outputs, and intermediate results.

   1. The **Network** tab summarizes your network isolation and encryption choices.
**Note**  
Feature importance and information in the **Performance** tab is only generated for the **Best model**.

   For more information about how the SHAP values help explain predictions based on feature importance, see the whitepaper [Understanding the model explainability](https://pages.awscloud.com/rs/112-TZM-766/images/Amazon.AI.Fairness.and.Explainability.Whitepaper.pdf). Additional information is also available in the [Model Explainability](clarify-model-explainability.md) topic in the SageMaker AI Developer Guide.