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 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.

  • 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.