Understand a model
Understand a model#
With the Understand a model task, you can look at a previously trained machine learning model and learn about it.
Select the “Understand a model” task.
Select a previously trained model.
Click the “Understand” button.
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.