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Train Anomaly Detection Model component

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This article describes how to use the Train Anomaly Detection Model component in Azure Machine Learning designer to create a trained anomaly detection model.

The component takes as input a set of parameters for an anomaly detection model and an unlabeled dataset. It returns a trained anomaly detection model, together with a set of labels for the training data.

For more information about the anomaly detection algorithms provided in the designer, see PCA-Based Anomaly Detection.

How to configure Train Anomaly Detection Model

  1. Add the Train Anomaly Detection Model component to your pipeline in the designer. You can find this component in the Anomaly Detection category.

  2. Connect one of the components designed for anomaly detection, such as PCA-Based Anomaly Detection.

    Other types of models are not supported. When you run the pipeline, you'll get the error "All models must have the same learner type."

  3. Configure the anomaly detection component by choosing the label column and setting other parameters specific to the algorithm.

  4. Attach a training dataset to the right-side input of Train Anomaly Detection Model.

  5. Submit the pipeline.

Results

After training is complete:

  • To view the model's parameters, right-click the component and select Visualize.

  • To create predictions, use the Score Model component with new input data.

  • To save a snapshot of the trained model, select the component. Then select the Register dataset icon under the Outputs+logs tab in the right panel.

Next steps

See the set of components available to Azure Machine Learning.

See Exceptions and error codes for the designer for a list of errors specific to the designer components.