GLMPredict Example 2: Gaussian Distribution Prediction - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Release Number
1.0
8.00
Published
May 2019
Language
English (United States)
Last Update
2019-11-22
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Product Category

The example uses the Gaussian modelto evaluate the prediction for new houses, comparing the prediction with the original price information with root mean square error evaluation (RMSE).

Input

housing_test
sn price lotsize bedrooms bathrms stories driveway recroom fullbase gashw airco garagepl prefarea homestyle
13 27000 1700 3 1 2 yes no no no no 0 no Classic
16 37900 3185 2 1 1 yes no no no yes 0 no Classic
25 42000 4960 2 1 1 yes no no no no 0 no Classic
38 67000 5170 3 1 4 yes no no no yes 0 no Eclectic
53 68000 9166 2 1 1 yes no yes no yes 2 no Eclectic
104 132000 3500 4 2 2 yes no no yes no 2 no bungalow
111 43000 5076 3 1 1 no no no no no 0 no Classic
117 93000 3760 3 1 2 yes no no yes no 2 no Eclectic
132 44500 3850 3 1 2 yes no no no no 0 no Classic
140 43000 3750 3 1 2 yes no no no no 0 no Classic
142 40000 2650 3 1 2 yes no yes no no 1 no Classic
157 60000 2953 3 1 2 yes no yes no yes 0 no Eclectic
161 63900 3162 3 1 2 yes no no no yes 1 no Eclectic
... ... ... ... ... ... ... ... ... ... ... ... ... ...

SQL Call

The canonical link specifies the default family link, which is "identity" for the Gaussian distribution.

```SELECT * FROM GLMPredict@coprocessor (
ON housing_test PARTITION BY ANY
ON glm_housing_model AS model DIMENSION
USING
Accumulate ('sn', 'price')
Family ('GAUSSIAN')
) AS dt;```

Output

This query returns the following table:

`SELECT * FROM glmpredict_housing ORDER BY 1;`

The fitted_value column gives the predicted home price.

sn price fitted_value
13 27000 37345.844
16 37900 43687.13175
25 42000 40902.028
38 67000 72487.6705
53 68000 79238.6937
104 132000 111528.007
111 43000 39102.8812
117 93000 66936.951
132 44500 41819.8865
140 43000 41611.7915
... ... ...

RMSE

This query returns the following table:

`SELECT SQRT(AVG(POWER(glmpredict_housing.price -   glmpredict_housing.fitted_value, 2))) AS RMSE FROM glmpredict_housing;`
rmse
10246.7521984348