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Evaluating Models

Learn about model evaluation tools

Now that you've successfully trained the model, you may want to test its performance before using it in a production environment. The Model Evaluation tool allows you to perform a cross validation on a specified model version. Once the evaluation is complete, you can view the various metrics that inform the model’s performance.

How It Works

Model Evaluation performs a K-split cross validation on data you used to train your custom model.

cross validation

In the cross validation process, it will:

  1. Set aside a random 1/K subset of the training data and designate as a test set;
  2. Train a new model with the remaining training data;
  3. Pass the test set data through this new model to make predictions;
  4. Compare the predictions against the test set’s actual labels;
  5. Repeat steps 1) through 4) across K splits to average out the evaluation results.


To run the evaluation on your custom model, it should meet the following criteria:

  • It should be a custom trained model version with:
    1. At least 2 concepts.
    2. At least 10 training inputs per concept (at least 50 inputs per concept is recommended).

The evaluation may result in an error if the model version doesn’t satisfy the requirements above.

Running Evaluation

You can run the evaluation on a specific version of your custom model in the Portal.

Go to your Application, click on your model of interest, and select the Versions tab. And to start the evaluation, simply click the Evaluate button for the specific model version

The evaluation may take up to 30 minutes. Once it is complete, the Evaluate button will become a View button.

Click the View button to see the evaluation results.

For more information on how to interpret the evaluation results, check the next Interpreting Evaluations section.