In this example, we again use our sample PostgreSQL database.First, we create and train the model using a subset of the customer_churn data, considering only female customers.
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CREATE MODEL mindsdb.adjust_customer_churn_modelFROM example_db (SELECT * FROM demo_data.customer_churn WHERE gender = 'Female')PREDICT churn;
On execution, we get:
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Query successfully completed
We can check its status using this command:
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SELECT *FROM mindsdb.modelsWHERE name = 'adjust_customer_churn_model';
Once the status is complete, we can query for predictions.
Here after adjusting the model, there are no significant changes to the predictions. However, the probability class for Yes and No values has been updated. The probability of a Yes value has increased slightly, while the probability of a No value has decreased.