Share via

Two-Class Bayes Point Machine


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.

ML Studio (classic) documentation is being retired and may not be updated in the future.

Creates a Bayes point machine binary classification model

Category: Machine Learning / Initialize Model / Classification


Applies to: Machine Learning Studio (classic) only

Similar drag-and-drop modules are available in Azure Machine Learning designer.

Module overview

This article describes how to use the Two-Class Bayes Point Machine module in Machine Learning Studio (classic), to create an untrained binary classification model.

The algorithm in this module uses a Bayesian approach to linear classification called the "Bayes Point Machine". This algorithm efficiently approximates the theoretically optimal Bayesian average of linear classifiers (in terms of generalization performance) by choosing one "average" classifier, the Bayes Point. Because the Bayes Point Machine is a Bayesian classification model, it is not prone to overfitting to the training data.

For more information, see Chris Bishop's post on the Microsoft Machine Learning blog: Embracing Uncertainty - Probabilistic Inference.

How to configure Two-Class Bayes Point Machine

  1. In Machine Learning Studio (classic), add the Two-Class Bayes Point Machine module to your experiment. You can find the module under Machine Learning, Initialize Model, Classification.

  2. For Number of training iterations, type a number to specify how often the message-passing algorithm iterates over the training data. Typically, the number of iterations should be set to a value in the range 5 – 100.

    The higher the number of training iterations, the more accurate the predictions; however, training will be slower.

    For most datasets, the default setting of 30 training iterations is sufficient for the algorithm to make accurate predictions. Sometimes accurate predictions can be made by using fewer iterations. For datasets with highly correlated features, you might benefit from more training iterations.

  3. Select the option, Include bias, if you want a constant feature or bias to be added to each instance in training and prediction.

    Including a bias is necessary when the data does not already contain a constant feature.

  4. Select the option, Allow unknown values in categorical features, to create a group for unknown values.

    If you deselect this option, the model can accept only the values that are contained in the training data.

    If you select this option and allow unknown values, the model might be less precise for known values, but it can provide better predictions for new (unknown) values.

  5. Add an instance of the Train Model module, and your training data.

  6. Connect the training data and the output of the Two-Class Bayes Point Machine module to the Train Model module, and choose the label column.

  7. Run the experiment.


After training is complete, right-click the output of the Train Model module to view the results:

  • To see a summary of the model's parameters, together with the feature weights learned from training, select Visualize.

  • To save the model for later use, right-click the output of Train MOdel, and select Save as Trained Model.

  • To make predictions, use the trained model as an input to the Score Model module.

    The untrained model can also be passed to Cross-Validate Model for cross-validation against a labeled data set.


To see how the Two-Class Bayes Point Machine is used in machine learning, see these sample experiments in the Azure AI Gallery:

Technical notes

This section contains implementation details and frequently asked questions about this algorithm.

Details from the original research and underlying theory are available in this paper (PDF): Bayes Point Machines, by Herbert, Graepe, and Campbell

However, this implementation improves on the original algorithm in several ways:

These improvements make the Bayes Point Machine classification model more robust and easier-to-use, and you can bypass the time-consuming step of parameter tuning.

Module parameters

Name Range Type Default Description
Number of training iterations >=1 Integer 30 Specify the number of iterations to use when training
Include bias Any Boolean True Indicate whether a constant feature or bias should be added to each instance
Allow unknown values in categorical features Any Boolean True If True, creates an additional level for each categorical column. Any levels in the test dataset that are not available in the training dataset are mapped to this additional level.


Name Type Description
Untrained model ILearner interface An untrained binary classification model

See also

Classification A-Z Module List