PREDICT_LOGISTIC_REG

Applies a logistic regression model on an input relation.

PREDICT_LOGISTIC_REG returns as a FLOAT the predicted class or the probability of the predicted class, depending on how the type parameter is set. You can cast the return value to INTEGER or another numeric type when the return is in the probability of the predicted class.

Syntax

PREDICT_LOGISTIC_REG ( input‑columns
        USING PARAMETERS model_name = 'model‑name'
            [, type = 'prediction‑type']
            [, cutoff = probability‑cutoff] 
            [, 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).
type

Type of prediction for logistic regression, one of the following:

  • response (default): Predicted values are 0 or 1.
  • probability: Output is the probability of the predicted category to be 1.
cutoff

Used in conjunction with the type parameter, a FLOAT between 0 and 1, exclusive. When type is set to response, the returned value of prediction is 1 if its corresponding probability is greater than or equal to the value of cutoff; otherwise, it is 0.

Default: 0.5
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 car_model,
            PREDICT_LOGISTIC_REG(mpg, cyl, disp, drat, wt, qsec, vs, gear, carb
                                 USING PARAMETERS model_name='myLogisticRegModel')
          FROM mtcars;
      car_model      | PREDICT_LOGISTIC_REG
---------------------+----------------------
 Camaro Z28          |                    0
 Fiat 128            |                    1
 Fiat X1-9           |                    1
 Ford Pantera L      |                    1
 Merc 450SE          |                    0
 Merc 450SL          |                    0
 Toyota Corona       |                    0
 AMC Javelin         |                    0
 Cadillac Fleetwood  |                    0
 Datsun 710          |                    1
 Dodge Challenger    |                    0
 Hornet 4 Drive      |                    0
 Lotus Europa        |                    1
 Merc 230            |                    0
 Merc 280            |                    0
 Merc 280C           |                    0
 Merc 450SLC         |                    0
 Pontiac Firebird    |                    0
 Porsche 914-2       |                    1
 Toyota Corolla      |                    1
 Valiant             |                    0
 Chrysler Imperial   |                    0
 Duster 360          |                    0
 Ferrari Dino        |                    1
 Honda Civic         |                    1
 Hornet Sportabout   |                    0
 Lincoln Continental |                    0
 Maserati Bora       |                    1
 Mazda RX4           |                    1
 Mazda RX4 Wag       |                    1
 Merc 240D           |                    0
 Volvo 142E          |                    1
(32 rows)

The following example shows how to use PREDICT_LOGISTIC_REG on an input table, using the match_by_pos parameter. Note that you can replace any of the column inputs with a constant that does not match an input column. In this example, column mpg was replaced with the constant 20: 

=> SELECT car_model,
            PREDICT_LOGISTIC_REG(20, cyl, disp, drat, wt, qsec, vs, gear, carb
                                 USING PARAMETERS model_name='myLogisticRegModel', match_by_pos='true')
          FROM mtcars;
      car_model     | PREDICT_LOGISTIC_REG
--------------------+----------------------
AMC Javelin         |                    0
Cadillac Fleetwood  |                    0
Camaro Z28          |                    0
Chrysler Imperial   |                    0
Datsun 710          |                    1
Dodge Challenger    |                    0
Duster 360          |                    0
Ferrari Dino        |                    1
Fiat 128            |                    1
Fiat X1-9           |                    1
Ford Pantera L      |                    1
Honda Civic         |                    1
Hornet 4 Drive      |                    0
Hornet Sportabout   |                    0
Lincoln Continental |                    0
Lotus Europa        |                    1
Maserati Bora       |                    1
Mazda RX4           |                    1
Mazda RX4 Wag       |                    1
Merc 230            |                    0
Merc 240D           |                    0
Merc 280            |                    0
Merc 280C           |                    0
Merc 450SE          |                    0
Merc 450SL          |                    0
Merc 450SLC         |                    0
Pontiac Firebird    |                    0
Porsche 914-2       |                    1
Toyota Corolla      |                    1
Toyota Corona       |                    0
Valiant             |                    0
Volvo 142E          |                    1
(32 rows)