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DESCRIBE statement

Description

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

DESCRIBE FEATURES

The DESCRIBE mindsdb.[name_of_your_predictor].features statement is used to display the way that the model encoded the data prior to training.

Syntax

DESCRIBE mindsdb.[name_of_your_predictor].features;

On execution:

+--------------+-------------+--------------+-------------+
| column       | type        | encoder      | role        |
+--------------+-------------+--------------+-------------+
| column_name  | column_type | encoder_used | column_role |
+--------------+-------------+--------------+-------------+

Where:

Description
[name_of_your_predictor] Name of the model to be described
column Columns used
type Type of data infered
encoder Encoder used
role Role for that column, it can be feature or target

Example

DESCRIBE mindsdb.home_rentals_model.features;

On execution:

+---------------------+-------------+----------------+---------+
| column              | type        | encoder        | role    |
+---------------------+-------------+----------------+---------+
| number_of_rooms     | categorical | OneHotEncoder  | feature |
| number_of_bathrooms | binary      | BinaryEncoder  | feature |
| sqft                | integer     | NumericEncoder | feature |
| location            | categorical | OneHotEncoder  | feature |
| days_on_market      | integer     | NumericEncoder | feature |
| neighborhood        | categorical | OneHotEncoder  | feature |
| rental_price        | integer     | NumericEncoder | target  |
+---------------------+-------------+----------------+---------+

DESCRIBE MODEL

The DESCRIBE mindsdb.[name_of_your_predictor].model statement is used to display the performance of the candidate models.

Syntax

DESCRIBE mindsdb.[name_of_your_predictor].model;

On execution:

+-----------------+-------------+---------------+----------+
| name            | performance | training_time | selected |
+-----------------+-------------+---------------+----------+
| candidate_model | performace  | training_time | selected |
+-----------------+-------------+---------------+----------+

Where:

Description
[name_of_your_predictor] Name of the model to be described
name Name of the candidate_model
performance Accuracy From 0 - 1 depending on the type of the model
training_time Time elapsed for the model training to be completed
selected 1 for the best performing model 0 for the rest

Example

DESCRIBE mindsdb.home_rentals_model.model;

On execution:

+------------+--------------------+----------------------+----------+
| name       | performance        | training_time        | selected |
+------------+--------------------+----------------------+----------+
| Neural     | 0.9861694189913056 | 3.1538941860198975   | 0        |
| LightGBM   | 0.9991920992432087 | 15.671080827713013   | 1        |
| Regression | 0.9983390488042778 | 0.016761064529418945 | 0        |
+------------+--------------------+----------------------+----------+

DESCRIBE MODEL

Syntax

DESCRIBE mindsdb.[name_of_your_predictor].ensemble;

On execution:

+-----------------+
| ensemble        |
+-----------------+
| {JSON}          |
+-----------------+

Where:

Description
ensemble JSON type object describing the parameters used to select best model candidate

Example

DESCRIBE mindsdb.home_rentals_model.ensemble;

On execution:

+----------------------------------------------------------------------+
| ensemble                                                             |
+----------------------------------------------------------------------+
| {
  "encoders": {
    "rental_price": {
      "module": "NumericEncoder",
      "args": {
        "is_target": "True",
        "positive_domain": "$statistical_analysis.positive_domain"
      }
    },
    "number_of_rooms": {
      "module": "OneHotEncoder",
      "args": {}
    },
    "number_of_bathrooms": {
      "module": "BinaryEncoder",
      "args": {}
    },
    "sqft": {
      "module": "NumericEncoder",
      "args": {}
    },
    "location": {
      "module": "OneHotEncoder",
      "args": {}
    },
    "days_on_market": {
      "module": "NumericEncoder",
      "args": {}
    },
    "neighborhood": {
      "module": "OneHotEncoder",
      "args": {}
    }
  },
  "dtype_dict": {
    "number_of_rooms": "categorical",
    "number_of_bathrooms": "binary",
    "sqft": "integer",
    "location": "categorical",
    "days_on_market": "integer",
    "neighborhood": "categorical",
    "rental_price": "integer"
  },
  "dependency_dict": {},
  "model": {
    "module": "BestOf",
    "args": {
      "submodels": [
        {
          "module": "Neural",
          "args": {
            "fit_on_dev": true,
            "stop_after": "$problem_definition.seconds_per_mixer",
            "search_hyperparameters": true
          }
        },
        {
          "module": "LightGBM",
          "args": {
            "stop_after": "$problem_definition.seconds_per_mixer",
            "fit_on_dev": true
          }
        },
        {
          "module": "Regression",
          "args": {
            "stop_after": "$problem_definition.seconds_per_mixer"
          }
        }
      ],
      "args": "$pred_args",
      "accuracy_functions": "$accuracy_functions",
      "ts_analysis": null
    }
  },
  "problem_definition": {
    "target": "rental_price",
    "pct_invalid": 2,
    "unbias_target": true,
    "seconds_per_mixer": 57024.0,
    "seconds_per_encoder": null,
    "expected_additional_time": 8.687719106674194,
    "time_aim": 259200,
    "target_weights": null,
    "positive_domain": false,
    "timeseries_settings": {
      "is_timeseries": false,
      "order_by": null,
      "window": null,
      "group_by": null,
      "use_previous_target": true,
      "horizon": null,
      "historical_columns": null,
      "target_type": "",
      "allow_incomplete_history": true,
      "eval_cold_start": true,
      "interval_periods": []
    },
    "anomaly_detection": false,
    "use_default_analysis": true,
    "ignore_features": [],
    "fit_on_all": true,
    "strict_mode": true,
    "seed_nr": 420
  },
  "identifiers": {},
  "accuracy_functions": [
    "r2_score"
  ]
}                                                                      |
+----------------------------------------------------------------------+

Unsure what it all means?

If you're unsure on how to DESCRIBE your model or understand the results feel free to ask us how to do it on the community Slack workspace.