View model details - Amazon SageMaker AI

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. ) from the left navigation pane to view the top-level Amazon SageMaker Studio Classic navigation menu.

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

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

  4. 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.

  5. 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.

    2. The Performance tab contains metrics statistics a confusion matrix.

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

    4. 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. Additional information is also available in the Model Explainability topic in the SageMaker AI Developer Guide.