19.3.13 Evaluating Model Performance

You can use the evaluate convenience method to evaluate various metrics for the model:

• JShell
• Java
• Python

opg4j> model.evaluate(fullGraph, testEdges).print()

model.evaluate(fullGraph,testEdges).print();

model.evaluate(full_graph,test_edges).print()

Similar to inferring labels, if the task is a classification task, you can add the decision threshold as an extra parameter:

• JShell
• Java
• Python

opg4j> model.evaluate(fullGraph, testEdges, 6f).print()

model.evaluate(fullGraph,testEdges, 6f).print();

model.evaluate(full_graph,test_edges, 6).print()

For a classification model, the output will be similar to the following:

+------------------------------------------+
| Accuracy | Precision | Recall | F1-Score |
+------------------------------------------+
| 0.8488   | 0.8523    | 0.831  | 0.8367   |
+------------------------------------------+

For a regression model, the output will be similar to the following:

+--------------------+
| MSE                |
+--------------------+
| 0.9573243436116953 |
+--------------------+

Note that for a classification model, the evaluateLabels method is also available and this is equivalent to the evaluate method.