# Evaluate a model

The **Evaluate a model** task measures the quality of a 
machine learning model that has already been trained using the data of the currently displayed sheet. The
quality of a model is described by metrics (e.g. accuracy).

The reported metrics depend on the way the model was trained. For example, if
the label of the model is a categorical column, the model is trained to do
classification, and the reported metrics will include metrics such as accuracy,
confusion tables, logloss, auc, pr-auc, and others. If the label of the model is
a numerical column, the model is trained to do regression, and the reported
metrics will include such as
[RMSE](https://en.wikipedia.org/wiki/Root-mean-square_deviation).

Note that the quality of a model is also available in the **Quality** tab of the
**Understand a model** task. However, the model quality in the **Understand a
model** is computed _during_ training.

See the
[YDF Glossary](https://ydf.readthedocs.io/en/stable/glossary/) for a detailed
explanation of each reported metrics.

Use this task as follows:

1.  Open a sheet with the test examples. The sheet should be in the
    [tabular format](sheet_format).
2.  Select the "Evaluate a model" task.
3.  Select a previously trained model.
4.  Click "Evaluate."

    After a few seconds, the evaluation window opens.