1.0 - 8.00 - Single_Tree_Predict Example 2: OutputResponseProbDist, Responses - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
English (United States)

Input

Input tables are from DecisionTree Example 1:
  • attribute_table: iris_attribute_test
  • model_table: iris_attribute_output

SQL Call

CREATE MULTISET TABLE singletree_predict AS (
  SELECT * FROM Single_Tree_Predict@coprocessor (
  ON iris_attribute_test AS attribute_table PARTITION BY pid ORDER BY attribute
  ON iris_attribute_output as model_table DIMENSION
  USING
  AttrTableGroupByColumns ('attribute')
  AttrTablePIDColumns ('pid')
  AttrTableValColumn ('attrvalue')
  OutputResponseProbDist ('true')
  Responses ('1','2','3')
  ) AS dt
) WITH DATA;

Output

This query returns the following table:

SELECT * FROM singletree_predict ORDER BY pid;
 pid | pred_label | prob_for_label_1 | prob_for_label_2 | prob_for_label_3 
-----+------------+------------------+------------------+------------------
   5 | 1          |          0.95348 |          0.02326 |          0.02326
  10 | 1          |          0.95348 |          0.02326 |          0.02326
  15 | 1          |          0.95348 |          0.02326 |          0.02326
  20 | 1          |          0.95348 |          0.02326 |          0.02326
  25 | 1          |          0.95348 |          0.02326 |          0.02326
  30 | 1          |          0.95348 |          0.02326 |          0.02326
  35 | 1          |          0.95348 |          0.02326 |          0.02326
  40 | 1          |          0.95348 |          0.02326 |          0.02326
  45 | 1          |          0.95348 |          0.02326 |          0.02326
  50 | 1          |          0.95348 |          0.02326 |          0.02326
  55 | 2          |          0.02632 |          0.94736 |          0.02632
  60 | 2          |          0.02632 |          0.94736 |          0.02632
  65 | 2          |          0.02632 |          0.94736 |          0.02632
  70 | 2          |          0.02632 |          0.94736 |          0.02632
  75 | 2          |          0.02632 |          0.94736 |          0.02632
  80 | 2          |          0.02632 |          0.94736 |          0.02632
  85 | 2          |          0.02632 |          0.94736 |          0.02632
  90 | 2          |          0.02632 |          0.94736 |          0.02632
  95 | 2          |          0.02632 |          0.94736 |          0.02632
 100 | 2          |          0.02632 |          0.94736 |          0.02632
 105 | 3          |          0.06250 |          0.12500 |          0.81250
 110 | 3          |          0.07692 |          0.07692 |          0.84616
 115 | 3          |          0.06250 |          0.06250 |          0.87500
 120 | 2          |          0.14286 |          0.57143 |          0.28571
 125 | 3          |          0.07692 |          0.07692 |          0.84616
 130 | 2          |          0.14286 |          0.57143 |          0.28571
 135 | 2          |          0.14286 |          0.57143 |          0.28571
 140 | 3          |          0.06250 |          0.12500 |          0.81250
 145 | 3          |          0.07692 |          0.07692 |          0.84616
 150 | 3          |          0.25000 |          0.25000 |          0.50000
(30 rows)