PREDICT_SVM_REGRESSOR
Applies an SVM model on an input table or view.
Important: Before using a machine learning function, be aware that all the ongoing transactions might be committed.
Syntax
PREDICT_SVM_REGRESSOR(input_columns USING PARAMETERS model_name='model_name' [, match_by_pos = 'method'])
Arguments
input_columns |
A comma-separated list of the columns to be used for prediction. |
Parameters
model_name='model_name' |
The name of the model. Model names are case-insensitive. |
match_by_pos= 'method' |
(Optional) Valid Values:
|
Return
Return data type: FLOAT |
Returns the predicted value. |
Examples
This example shows how you can use the PREDICT_SVM_REGRESSOR function on the faithful table:
=> SELECT PREDICT_SVM_REGRESSOR(waiting USING PARAMETERS model_name='mySvmRegModel') FROM faithful ORDER BY id; PREDICT_SVM_REGRESSOR -------------------- 4.06488248694445 2.30392277646291 3.71269054484815 2.867429883817 4.48751281746003 2.37436116488217 4.69882798271781 4.48751281746003 2.09260761120512
. . . (272 rows)
This example shows how you can use the PREDICT_SVM_REGRESSOR function on the faithful table, using the match_by_pos
parameter. Note that you can any of the column inputs with a constant that does not match an input column. In this example, the waiting column was replaced with the constant 40:
=> SELECT PREDICT_SVM_REGRESSOR(40 USING PARAMETERS model_name='mySvmRegModel', match_by_pos = 'true') FROM faithful ORDER BY id; PREDICT_SVM_REGRESSOR -------------------- 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324 1.31778533859324
. . . (272 rows)