MySQL HeatWave User Guide
In MySQL HeatWave, there are two types of learning for anomaly detection models: unsupervised and semi-supervised.
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
.
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.
Learn more about the following:
Learn how to Prepare Data for an Anomaly Detection Model.