6.7.4.2 Anomaly Detection Learning Types

In MySQL HeatWave, there are two types of learning for anomaly detection models: unsupervised and semi-supervised.

Unsupervised Anomaly Detection

When running an unsupervised anomaly detection model, the machine learning algorithm requires no labeled data. When training the model, the target_column_name parameter must be set to NULL.

Semi-supervised Anomaly Detection

MySQL 9.0.1-u1 introduces support for semi-supervised learning. This type of machine learning algorithm uses a specific set of labeled data along with unlabeled data to detect anomalies. To enable this, use the experimental and semisupervised options. The target_column_name parameter must specify a column whose only allowed values are 0 (normal), 1 (anomalous), and NULL (unlabeled). All rows are used to train the unsupervised component, while the rows with a value different than NULL are used to train the supervised component.

What's Next

• Learn more about the following:

  • Anomaly Detection Algorithm Model Types

  • Anomaly Detection for Logs

• Learn how to Prepare Data for an Anomaly Detection Model.