Example: TD_GLMPredict Using Credit Data
This example takes credit data and uses TD_GLM function to get a model. You can view the input and output in the TD_GLM example.
TD_GLMPredict Call for Credit Data
CREATE VOLATILE TABLE vt_glm_predict_credit_ex AS (
SELECT * from TD_GLMPredict (
ON credit_ex_merged AS INPUTTABLE
ON td_glm_output_credit_ex AS Model DIMENSION
USING
IDColumn ('ID')
Accumulate('Outcome')
) AS dt
) WITH DATA
ON COMMIT PRESERVE ROWS;
TD_GLMPredict Output for Credit Data
| ID | Prediction | Outcome |
|---|---|---|
| 61 | 1 | 1 |
| 297 | 0 | 0 |
| 631 | 0 | 0 |
| 122 | 1 | 1 |
| ... | ... | ... |
Example: TD_GLMPredict Using Housing Data
This example takes raw housing data, and does the following:
- Uses TD_ScaleFit to standardize the data.
- Uses TD_ScaleTransform to transform the data.
- Uses TD_GLM to get a model.
- Uses TD_GLMPredict to predict target values.
You can view the input and output of steps 1 through 3 in the TD_GLM example.
TD_GLMPredict Call for Housing Data
CREATE VOLATILE TABLE vt_predict_cal_ex AS (
SELECT * from TD_GLMPredict (
ON cal_housing_ex_scaled AS INPUTTABLE
ON td_glm_cal_ex AS Model DIMENSION
USING
IDColumn ('ID')
Accumulate('MedHouseVal')
) AS dt
) WITH DATA
ON COMMIT PRESERVE ROWS;
TD_GLMPredict Output for Housing Data
| ID | Prediction | MedHouseVal |
|---|---|---|
| 2833 | 1.5762 | 0.6 |
| 5328 | 2.29801 | 2.775 |
| 5300 | 1.82705 | 3.5 |
| 12433 | 0.863867 | 0.664 |
| ... | ... | ... |