Models
FINETUNE Statement

Description

The FINETUNE statement lets you retrain a model with additional training data.

Imagine you have a model that was trained with a certain dataset. Now there is more training data available and you wish to retrain this model with a new dataset. The FINETUNE statement lets you partially retrain the model, so it takes less time and resources than the RETRAIN statement. In the machine learning literature, this is also referred to as fine-tuning a model.

Syntax

Here is the syntax:

FINETUNE project_name.model_name
FROM integration_name
    (SELECT column_name, ... FROM table_name)
[USING
    key = value,
    ...];

Where:

ExpressionsDescription
project_nameName of the project where the model resides.
model_nameName of the model to be retrained.
integration_nameName of the integration created using the CREATE DATABASE statement or file upload.
(SELECT column_name, ... FROM table_name)Selecting additional data to be used for retraining.
USING key = valueOptional. The USING clause lets you pass multiple parameters to the FINETUNE statement.

Model Versions

Every time the model is retrained/adjusted, its new version is created with an incremented version number. Unless overridden, the most recent version becomes active when training completes.

You can query for all model versions like this:

SELECT *
FROM project_name.models_versions;

For more information on managing model versions, check out our docs here.

Example 1: Regression Model

In this example, we use our sample PostgreSQL database. You can connect to it like this:

CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
    "user": "demo_user",
    "password": "demo_password",
    "host": "3.220.66.106",
    "port": "5432",
    "database": "demo"
    };

First, we create and train the model using a subset of the home_rentals data, considering properties that have been on the market less than 10 days.

CREATE MODEL mindsdb.adjust_home_rentals_model
FROM example_db
    (SELECT * 
    FROM demo_data.home_rentals 
    WHERE days_on_market < 10)
PREDICT rental_price;

On execution, we get:

Query successfully completed

We can check its status using this command:

SELECT *
FROM mindsdb.models
WHERE name = 'adjust_home_rentals_model';

Once the status is complete, we can query for predictions.

SELECT rental_price, rental_price_explain 
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;

On execution, we get:

+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price  | rental_price_explain                                                                                                                          |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2621          | {"predicted_value": 2621, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 2523, "confidence_upper_bound": 2719} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+

Let’s adjust this model with more training data. Now we consider properties that have been on the market for 10 or more days.

FINETUNE mindsdb.adjust_home_rentals_model
FROM example_db
    (SELECT * 
    FROM demo_data.home_rentals 
    WHERE days_on_market >= 10);

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_home_rentals_model';

On execution, we get:

+---------------------------+-----------+---------+--------+---------+----------+
| name                      | engine    | project | active | version | status   |
+---------------------------+-----------+---------+--------+---------+----------+
| adjust_home_rentals_model | lightwood | mindsdb | false  | 1       | complete |
| adjust_home_rentals_model | lightwood | mindsdb | true   | 2       | complete |
+---------------------------+-----------+---------+--------+---------+----------+

Please note that the longer the property is on the market, the lower its rental price. Hence, we can expect the rental_price prediction to be lower.

SELECT rental_price, rental_price_explain 
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;

On execution, we get:

+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price  | rental_price_explain                                                                                                                          |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2055          | {"predicted_value": 2055, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 1957, "confidence_upper_bound": 2153} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+

Example 2: Classification Model

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.