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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-explanations-ml-explain-table.html
ML_EXPLAIN_TABLE explains predictions for an entire table of unlabeled data. We recommend limiting the input table to a maximum of 100 rows. If the input table has more than ten columns, limit it to ten rows. Some of these columns include: ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-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/mysql-ai/9.5/en/mys-ai-aml-forecasting-model-types.html
This topic describes the types of forecasting models supported by AutoML. Univariate Models In a univariate model, you define one numeric column as an endogenous variable, specified as a JSON_ARRAY. For example, you forecast the rainfall for the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-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 Additional AutoML Requirements. To start using AutoML with sample datasets, see Machine Learning ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-load-model.html
You must load a machine learning model from the model catalog before running AutoML routines other than ML_TRAIN. A model remains loaded and can be called repetitively by AutoML routines until it is unloaded using the ML_MODEL_UNLOAD routine, or ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-ml-model-metadata.html
The model_metadata column in the model catalog allows you to view detailed information on trained models. For example, you can view the algorithm used to train the model, the columns in the training table, and values for the model explanation. When ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-catalog-table.html
The MODEL_CATALOG table (ML_SCHEMA_user_name.MODEL_CATALOG) has the following columns: model_id A primary key, and a unique auto-incrementing numeric identifier for the model. The model handle is generated or set by the user when the ML_TRAIN ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-catalog.html
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, where the user_name is the name of the owning user. The fully qualified name of the model ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-handles.html
When ML_TRAIN trains a model, you have the option to specify a name for the model, which is the model handle. If you do not specify a model handle name, a model handle is automatically generated that is based on the database name, input table name, ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-management.html
The following sections describe how to manage your machine learning models.