Score Model module

This article describes a module of the visual interface (preview) for Azure Machine Learning service.

Use this module to generate predictions using a trained classification or regression model.

How to use

  1. Add the Score Model module to your experiment.

  2. Attach a trained model and a dataset containing new input data.

    The data should be in a format compatible with the type of trained model you are using. The schema of the input dataset should also generally match the schema of the data used to train the model.

  3. Run the experiment.


After you have generated a set of scores using Score Model:

  • To generate a set of metrics used for evaluating the model’s accuracy (performance). you can connect the scored dataset to Evaluate Model,
  • Right-click the module and select Visualize to see a sample of the results.
  • Save the results to a dataset.

The score, or predicted value, can be in many different formats, depending on the model and your input data:

  • For classification models, Score Model outputs a predicted value for the class, as well as the probability of the predicted value.
  • For regression models, Score Model generates just the predicted numeric value.
  • For image classification models, the score might be the class of object in the image, or a Boolean indicating whether a particular feature was found.

Publish scores as a web service

A common use of scoring is to return the output as part of a predictive web service. For more information, see this tutorial on how to create a web service based on an experiment in Azure Machine Learning:

Next steps

See the set of modules available to Azure Machine Learning service.