Search Results
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-anomaly-detection.html
Anomaly detection, which is also known as outlier detection, is the machine learning task that finds unusual patterns in data. The following tasks use datasets generated by OCI GenAI using Meta Llama Models. The anomaly detection use-cases are to ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-classification-score.html
After generating predictions and explanations, you can score the model to assess its reliability. For a list of scoring metrics you can use with classification models, see Classification Metrics. For this use case, you use the test dataset for ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ease-of-use.html
With MySQL HeatWave AutoML and a set of training data in a MySQL HeatWave DB system, you can train a predictive machine learning model with a single call to the ML_TRAIN SQL routine. For example: CALL sys.ML_TRAIN('heatwaveml_bench.census_train', ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-explanations.html
Prediction explanations are generated by running ML_EXPLAIN_ROW or ML_EXPLAIN_TABLE on unlabeled data. The data must have the same feature columns as the data used to train the model. Prediction explanations are similar to model explanations, but ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-get-started.html
The topics in this section go through the process of training and using a machine learning model. Before going through these tasks, make sure to Review Requirements for MySQL HeatWave and Additional MySQL HeatWave AutoML Requirements. To start ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-model-types.html
When training a model, use the ML_TRAIN model_list and exclude_model_list options to specify the training models to consider or exclude. The Model Metadata includes the algorithm_name field, which defines the model type. Recommendation models that ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-model-catalog.html
MySQL HeatWave AutoML stores machine learning models in a model catalog. MySQL HeatWave AutoML creates a model catalog for any user that creates a machine learning model. The MODEL_CATALOG table is created in a schema named ML_SCHEMA_user_name, ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-monitoring.html
You can monitor the status of MySQL HeatWave AutoML by querying the rapid_ml_status variable or by querying the ML_STATUS column of the performance_schema.rpd_nodes table. Before You Begin Review how to Track Progress for MySQL HeatWave AutoML ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-prepare-data-overview.html
MySQL HeatWave AutoML works with labeled and unlabeled data to train and score machine learning models. Labeled Data Labeled data is data that has values associated with it. It has feature columns and a target column (the label), as illustrated in ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-regression-score.html
After generating predictions and explanations, you can score the model to assess its reliability. For a list of scoring metrics you can use with regression models, see Regression Metrics. For this use case, you use the test dataset for validation.