PREDICT_LINEAR_REG
Applies a linear regression 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_LINEAR_REG ( col1, col2, ... coln USING PARAMETERS model_name = 'name_of_model' [, match_by_pos = 'method'])
Arguments
col1, col2, ..., coln |
The columns to use from the input table or view. |
Parameters
model_name = 'name_of_model' |
The name of the linear regression model. |
match_by_pos= 'method' |
(Optional) Valid Values:
|
Return
Return data type:FLOAT |
Returns the predicted value. |
Examples
The following example shows how you can use the PREDICT_LINEAR_REG function on an input table.
=> SELECT PREDICT_LINEAR_REG(waiting USING PARAMETERS model_name='linear_reg_faithful')FROM faithful ORDER BY id; PREDICT_LINEAR_REG -------------------- 4.15403481386324 2.18505296804024 3.76023844469864 2.8151271587036 4.62659045686076 2.26381224187316 4.86286827835952 4.62659045686076 1.94877514654148 4.62659045686076 2.18505296804024 . . . (272 rows)
The following example shows how you can use the PREDICT_LINEAR_REG function on an input table, using the match_by_pos
parameter. Note that you can replace the column argument with a constant that does not match an input column:
=> SELECT PREDICT_LINEAR_REG(55 USING PARAMETERS model_name='linear_reg_faithful', match_by_pos='true')FROM faithful ORDER BY id; PREDICT_LINEAR_REG -------------------- 2.28552115094171 2.28552115094171 2.28552115094171 2.28552115094171 2.28552115094171 2.28552115094171 2.28552115094171
. . . (272 rows)