RF_PREDICTOR_IMPORTANCE

Measures the importance of the predictors in a random forest model using the Mean Decrease Impurity (MDI) approach.

Syntax

RF_PREDICTOR_IMPORTANCE ( USING PARAMETERS model_name='model‑name'
	                                    [, tree_id=tree‑id] )

Parameter Settings

Parameter name Set to…
model_name

Identifies the model that is stored as a result of the training, where model‑name must be of type rf_classifier or rf_regressor.
tree‑id

Identifies the tree to process, an integer between 0 and n-1, where n is the number of trees in the forest. If you omit this parameter, the function uses all trees to measure importance values.

Privileges

Non-superusers: USAGE privileges on the model

Examples

This example shows how you can use the RF_PREDICTOR_IMPORTANCE function.

=> SELECT RF_PREDICTOR_IMPORTANCE ( USING PARAMETERS model_name = 'myRFModel', tree_id=1);
predictor_index | predictor_name | importance_value
----------------+----------------+-------------------
0               | sepal_length   |                 0
1               | sepal_width    |                 0
2               | petal_length   | 0.200289855072464
3               | petal_wdith    |                 0
(4 rows)

See Also