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Train a model from PostgreSQL database


Train new model

To train a new model, you will need to INSERT a new record inside the mindsdb.predictors table.

How to create the mindsb schema and tables

Note that after connecting MindsDB and PostgreSQL, on start, the MindsDB server will automatically create the mindsDB schema and add the predictors table.


Don't forget to install the MySQL foreign data wrapper as explained in connect your data section.

The INSERT query for training a new model is quite simple, e.g.:

INSERT INTO mindsdb.predictors(name, predict, select_data_query, training_options)
VALUES ('model_name', 'target_variable', 'SELECT * FROM table_name', '{"additional_training_params:value"}');
The values provided in the INSERT query are:

  • name (string) -- The name of the model.
  • predict (string) -- The feature you want to predict. To predict multiple features, include a comma separated string, e.g. 'feature1,feature2'.
  • select_data_query (string) -- The SELECT query that will ingest the data to train the model.
  • training_options (JSON as comma separated string) -- optional value that contains additional training parameters. For a full list of parameters, check the PredictorInterface.

Train model from psql client


To train timeseries model, check out the timeseries example.

Train new model example

The following example shows you how to train a new model from a psql client. The table used for training the model is the Airline Passenger Satisfaction dataset.

INSERT INTO mindsdb.predictors(name, predict, select_data_query) VALUES('airline_survey_model', 'satisfaction', 'SELECT * FROM airline_passenger_satisfaction');
This INSERT query will train a new model called airline_survey_model that predicts the passenger satisfaction value.

Model training status

To check that the training finished successfully, you can SELECT from the mindsdb.predictors table and get the training status, e.g.:

SELECT * FROM mindsdb.predictors WHERE name='<model_name>';

Training model status

That's it 🎉 🏆 💻

You have successfully trained a new model from PostgreSQL database. The next step is to get predictions by querying the model.