# Understand a model

With the **Understand a model** task, you can look at a previously trained
machine learning model and learn about it.

1.  Select the "Understand a model" task.
2.  Select a previously trained model.
3.  Click the "Understand" button.
4.  After a few seconds, the analysis window open.

The model understanding window contains several tabs:

-   **Summary:** The summary tab shows the model filename, training date, target
    column and source columns (a.k.a. input features).
-   **Quality:** The quality tab shows how good the model performs by reporting
    the model's
    [evaluation metrics](https://ydf.readthedocs.io/en/latest/metrics.html)
    computed during training on the validation data (or something equivalent).
-   **Dataset:** The dataset tab shows statistics about the columns in the
    dataset.
-   **Variable importance:** What input features matter to the model. See
    [variable importances](https://ydf.readthedocs.io/en/latest/cli_user_manual.html#variable-importances).
-   **Predictions:** Plots of the prediction results, if any. The "Predictions"
    tab is only populated if you check the "Use the current sheet data for
    analysis" checkbox.
-   **Plot model:** A representation of the model. Currently, only the *Decision
    Tree* model can be plotted.

<!-- TODO(gbm): Explain dataset and variable importance reading. -->
<!-- TODO(gbm): Explain use sheet data checkbox. -->
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