PREDICT_LINEAR_REG

Applies a linear regression model on an input relation and returns the predicted value as a FLOAT.

Syntax

PREDICT_LINEAR_REG ( input‑columns
        USING PARAMETERS model_name = 'model‑name' [, match_by_pos = match‑by‑position] )

Arguments

input‑columns

Comma-separated list of columns to use from the input relation, or asterisk (*) to select all columns.

Parameters

model_name Name of the model (case-insensitive).
match_by_pos

Boolean value that specifies how input columns are matched to model features:

  • false (default): Match by name.
  • true: Match by the position of columns in the input columns list.

Examples

=> SELECT PREDICT_LINEAR_REG(waiting USING PARAMETERS model_name='myLinearRegModel')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 to 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)