Get Multi-Label Model Metrics

The call that you make to get model metrics is always the same format, but the response varies depending on the type of model for which you retrieve metrics.

Response Body

Name	Type	Description	Available Version
`createdAt`	date	Date and time that the model was created.	2.0
`id`	string	ID of the model. Contains letters and numbers.	2.0
`language`	string	Model language inherited from the dataset language. Default is `N/A`.	2.0
`metricsData`	object	Model metrics values.	2.0
`object`	string	Object returned; in this case, `metrics`.	2.0

metricsData Response Body

Name	Type	Description	Available Version
`confusionMatrices`	object	This object contains: A string for each label in the model. For each label, a two-element array. For each element in the array, a two-element integer array that contains the confusion matrix values. These values are the correct and incorrect classifications for the label based on testing done during the training process. Use this field to build a binary confusion matrix for each label in the model.	2.0
`f1`	array	Array of float arrays that contains the weighted average of precision and recall for each label in the dataset. The corresponding label for each value in this array can be found in the `labels` array. For example, the f1 score in the first array corresponds to the first label in the `labels` array.	2.0
`labels`	array	Array of strings that contains the dataset labels. These labels correspond to the values in the `f1` array and the `confusionMatrix` array.	2.0
`testAccuracies`	array	Array of floats that specify the accuracy of the test data for each label. From your initial dataset, by default, 10% of the data is set aside and isn't used during training to create the model. This 10% is then sent to the model for prediction. How often the correct prediction is made with this 10% is reported for each label in the `testAccuracies` array.	2.0
`trainingAccuracies`	array	Array of floats that specify the accuracy of the training data for each label. By default, 90% of the data from your dataset is left after the test accuracy set is set aside. This 90% is then sent to the model for prediction. How often the correct prediction is made with this 90% is reported for each label in the `trainingAccuracies` array.	2.0

Use the confusionMatrices array to build a binary confusion matrix for each label in a model. Here's what the confusion matrices for the first three labels in the example results might look like.

	tennis-ball	not tennis-ball
tennis-ball	43	0
not tennis-ball	0	12

	baseball-bat	not baseball-bat
baseball-bat	44	2
not baseball-bat	0	9

	tennis-court	not tennis-court
tennis-court	41	2
not tennis-court	0	12