Train Anomaly Detection Model

Updated: September 21, 2017

Trains an anomaly detection model on a training set

Category: Machine Learning / Train

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

The module takes as input a set of model parameters for anomaly detection model, such as that produced by the One-Class Support Vector Machine module, 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 Azure Machine Learning, see these topics:

• One-Class Support Vector Machine

• PCA-Based Anomaly Detection

1. Add the Train Anomaly Detection Model module to your experiment. You can find the module under Machine Learning, Train.

2. To the left input, connect one of the modules designed for anomaly detection, such as PCA-Based Anomaly Detection or One-Class Support Vector Machine.

   Other types of models are not supported; on running the experiment you will get the error: All models must have the same learner type.

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

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

5. Run the experiment.

6. When training is complete, right-click the module and select Visualize to view the model's parameters.

   You can also save the trained model, or use it with Score Model to create predictions.

For an example of how anomaly detection can be implemented in Azure Machine Learning, see these sample experiments in the Model Gallery:

• The On-line Fraud Detection sample provides a detailed walkthrough of an anomaly detection scenario, including how to engineer features and interpret the results of an algorithm.

• The Anomaly Detection: Credit Risk sample illustrates how to use the One-Class Support Vector Machine and PCA-Based Anomaly Detection modules for fraud detection.

For a list of all module errors, see Module Error Codes.

Train
Anomaly Detection