Describe a Model

Description

The DESCRIBE statement is used to display the attributes of an existing model.

The available options to describe a model depend on the underlying engine.

Syntax

Here is how to retrieve general information on the model:

DESCRIBE model_name;

Or:

DESCRIBE MODEL model_name;

This command is similar to the below command:

SELECT *
FROM models
WHERE name = 'model_name';

One difference between these two commands is that DESCRIBE outputs an additional column that stores all available options to describe a model, depending on the underlying engine.

Examples

Lightwood Models

MindsDB uses the Lightwood engine by default. Let’s see how to describe such models.

DESCRIBE [MODEL] home_rentals_model;

On execution we get:

+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+
| tables                               | NAME               | ENGINE    | PROJECT | ACTIVE | VERSION | STATUS   | ACCURACY | PREDICT      | UPDATE_STATUS | MINDSDB_VERSION | ERROR  | SELECT_DATA_QUERY                    | TRAINING_OPTIONS           | TAG    |
+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+
| ["info","features","model","jsonai"] | home_rentals_model | lightwood | mindsdb | true   | 1       | complete | 0.999    | rental_price | up_to_date    | 23.4.4.0        | [NULL] | SELECT * FROM demo_data.home_rentals | {'target': 'rental_price'} | [NULL] |
+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+

The tables output column lists all available options to describe a model.

DESCRIBE info

DESCRIBE [MODEL] home_rentals_model.info;

The above command returns the following output columns:

Name	Description
accuracies	It lists the accuracy function used to evaluate the model and the achieved score.
column_importances	It lists all feature-type columns and assigns importance values.
outputs	The target column.
inputs	All the feature columns.

DESCRIBE [MODEL] home_rentals_model.info;

The above command returns the following output columns:

Name	Description
accuracies	It lists the accuracy function used to evaluate the model and the achieved score.
column_importances	It lists all feature-type columns and assigns importance values.
outputs	The target column.
inputs	All the feature columns.

DESCRIBE [MODEL] home_rentals_model.features;

The above command returns the following output columns:

Name	Description
column	Data columns that were used to create the model.
type	Data type of the column.
encoder	Encoder type used for the column.
role	Role of the column (feature or target).

DESCRIBE [MODEL] home_rentals_model.model;

The above command returns the following output columns:

Name	Description
name	Name of the candidate model.
performance	Accuracy value from 0 to 1, depending on the type of the model.
training_time	Time elapsed for the training of the model.
selected	1 for the best performing model and 0 for the rest.
accuracy_functions	It defines the accuracy function used to evaluate the model. It stores the r2_score value for regression predictions, the balanced_accuracy_score value for classification predictions, and the bounded_ts_accuracy value for time series predictions. The values vary between 0 and 1, where 1 indicates a perfect predictor, based on results obtained for a held out portion of data.

DESCRIBE [MODEL] home_rentals_model.jsonai;

The above command returns the following output column:

Name	Description
ensemble	Object of the JSON type describing the parameters used to select the best candidate model.

NLP Models

MindsDB offers NLP models that utilize either Hugging Face or OpenAI engines. Let’s see how to describe such models.

DESCRIBE [MODEL] sentiment_classifier;

On execution we get:

+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| tables              | NAME                 | ENGINE | PROJECT | ACTIVE | VERSION | STATUS   | ACCURACY | PREDICT   | UPDATE_STATUS | MINDSDB_VERSION | ERROR  | SELECT_DATA_QUERY | TRAINING_OPTIONS                                                                                                                                                                                                                                                                                              | TAG    |
+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| ["args","metadata"] | sentiment_classifier | openai | mindsdb | true   | 1       | complete | [NULL]   | sentiment | up_to_date    | 23.1.3.2        | [NULL] | [NULL]            | {'target': 'sentiment', 'using': {'prompt_template': 'describe the sentiment of the reviews\n                     strictly as "positive", "neutral", or "negative".\n                     "I love the product":positive\n                     "It is a scam":negative\n                     "{{review}}.":'}} | [NULL] |
+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+

The tables output column lists all available options to describe a model.

DESCRIBE args

DESCRIBE [MODEL] sentiment_classifier.args;

The above command returns the following output columns:

Name	Description
key	It stores parameters, such as prompt_template and target.
value	It stores parameter values.

DESCRIBE [MODEL] sentiment_classifier.args;

The above command returns the following output columns:

Name	Description
key	It stores parameters, such as prompt_template and target.
value	It stores parameter values.

DESCRIBE [MODEL] sentiment_classifier.metadata;

The above command returns the following output columns:

Name	Description
key	It stores metadata parameters.
value	It stores parameter values.

Nixtla Models

MindsDB integrates Nixtla engines, such as StatsForecast, NeuralForecast, and HierarchicalForecast. Let’s see how to describe models based on Nixtla engines.

DESCRIBE [MODEL] quarterly_expenditure_forecaster;

On execution we get:

+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| tables                      | NAME                             | ENGINE        | PROJECT | ACTIVE | VERSION | STATUS   | ACCURACY | PREDICT     | UPDATE_STATUS | MINDSDB_VERSION | ERROR  | SELECT_DATA_QUERY                     | TRAINING_OPTIONS                                                                                                                                    | TAG    |
+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| ["info","features","model"] | quarterly_expenditure_forecaster | statsforecast | mindsdb | true   | 1       | complete | [NULL]   | expenditure | up_to_date    | 23.4.4.0        | [NULL] | SELECT * FROM historical_expenditures | {'target': 'expenditure', 'using': {}, 'timeseries_settings': {'is_timeseries': True, 'order_by': 'month', 'horizon': 3, 'group_by': ['category']}} | [NULL] |
+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+

The tables output column lists all available options to describe a model.

DESCRIBE info

DESCRIBE [MODEL] quarterly_expenditure_forecaster.info;

The above command returns the following output columns:

Name	Description
accuracies	It lists the chosen model name and its accuracy score.
outputs	The target column.
inputs	All the feature columns.

DESCRIBE [MODEL] quarterly_expenditure_forecaster.info;

The above command returns the following output columns:

Name	Description
accuracies	It lists the chosen model name and its accuracy score.
outputs	The target column.
inputs	All the feature columns.

DESCRIBE [MODEL] quarterly_expenditure_forecaster.features;

The above command returns the following output columns:

Name	Description
ds	It defines intervals between records. For example, here, we’ve got monthly expenditure records.
y	It stores the target column.
unique_id	It stores columns listed in the GROUP BY clause. It defines the column(s) in the dataset by which you can split it into multiple time series that track the same process or value for different entities or groups.

DESCRIBE [MODEL] quarterly_expenditure_forecaster.model;

The above command returns the following output columns:

Name	Description
model_name	It is the chosen model name.
frequency	It is the frequency
season_length	It indicates how many measurements until the next season occurs. For example, a time series with monthly measurements and a season length of 12 means that, every 12 months, a new season occurs. It can have a very strong effect on the final model’s performance.
hierarchy	It defines whether HierarchicalForecast is used (true) or not (false).

Other Models

Models that utlize LangChain or are brought to MindsDB via MLflow can be described as follows:

DESCRIBE [MODEL] other_model;

The above command returs ["info"] in its first output column.

DESCRIBE [MODEL] other_model.info;

The above command lists basic model information.

If you need more information on how to DESCRIBE [MODEL] or understand the results, feel free to ask us on the community Slack workspace.