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.