Train Anomaly Detection Model
Important
Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.
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Trains an anomaly detection model on a training set
Category: Machine Learning / Train
Note
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 Train Anomaly Detection Model module in 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 Machine Learning, see these topics:
How to configure Train Anomaly Detection Model
Add the Train Anomaly Detection Model module to your experiment in Studio (classic). You can find the module under Machine Learning, in the Train category.
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.
Configure the anomaly detection module by choosing the label column and setting other parameters specific to the algorithm.
Attach a training dataset to the right-hand input of Train Anomaly Detection Model.
Run the experiment.
Results
After training is complete:
To view the model's parameters, right-click the module and select Visualize.
To create predictions, use Score Model with new input data.
To save a snapshot of the trained model, right-click the Trained Model output, and select Save As.
Examples
For an example of how anomaly detection is implemented in Machine Learning, see the Azure AI Gallery:
On-line Fraud Detection: Provides a detailed walkthrough of an anomaly detection scenario, including how to engineer features and interpret the results of an algorithm.
Anomaly Detection: Credit Risk: Illustrates how to use the One-Class Support Vector Machine and PCA-Based Anomaly Detection modules for fraud detection.
Expected inputs
| Name | Type | Description |
|---|---|---|
| Untrained model | ILearner interface | Untrained anomaly detection model |
| Dataset | Data Table | Input data source |
Outputs
| Name | Type | Description |
|---|---|---|
| Trained model | ILearner interface | Trained anomaly detection model |
Exceptions
| Exception | Description |
|---|---|
| Error 0003 | Exception occurs if one or more of inputs are null or empty. |
For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.
For a list of API exceptions, see Machine Learning REST API Error Codes.