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.
CREATE MODEL mindsdb.adjust_customer_churn_model
FROM example_db
(SELECT *
FROM demo_data.customer_churn
WHERE gender = 'Female')
PREDICT churn;
On execution, we get:
Query successfully completed
We can check its status using this command:
SELECT *
FROM mindsdb.models
WHERE name = 'adjust_customer_churn_model';
Once the status is complete, we can query for predictions.
SELECT churn, churn_explain
FROM mindsdb.adjust_customer_churn_model
WHERE seniorcitizen = 0
AND partner = 'Yes'
AND dependents = 'No'
AND tenure = 1
AND phoneservice = 'No'
AND multiplelines = 'No phone service'
AND internetservice = 'DSL';
On execution, we get:
+
| churn | churn_explain |
+
| No | {"predicted_value": "No", "confidence": 0.9887640449438202, "anomaly": null, "truth": null, "probability_class_No": 0.934, "probability_class_Yes": 0.066} |
+
Let’s adjust this model with more training data. Now we also consider male customers.
FINETUNE mindsdb.adjust_customer_churn_model
FROM example_db
(SELECT *
FROM demo_data.customer_churn
WHERE gender = 'Male');
On execution, we get:
Query successfully completed
To check the status and versions of the model, run this command:
SELECT name, engine, project, active, version, status
FROM mindsdb.models_versions
WHERE name = 'adjust_customer_churn_model';
On execution, we get:
+
| name | engine | project | active | version | status |
+
| adjust_customer_churn_model | lightwood | mindsdb | false | 1 | complete |
| adjust_customer_churn_model | lightwood | mindsdb | true | 2 | complete |
+
Let’s query for a prediction again.
SELECT churn, churn_explain
FROM mindsdb.adjust_customer_churn_model
WHERE seniorcitizen = 0
AND partner = 'Yes'
AND dependents = 'No'
AND tenure = 1
AND phoneservice = 'No'
AND multiplelines = 'No phone service'
AND internetservice = 'DSL';
On execution, we get:
+
| churn | churn_explain |
+
| No | {"predicted_value": "No", "confidence": 0.9887640449438202, "anomaly": null, "truth": null, "probability_class_No": 0.9294, "probability_class_Yes": 0.0706} |
+
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.
