Input
Input tables are from DecisionTree Example 1:
- attribute_table: iris_attribute_test
- model_table: iris_attribute_output
SQL Call
CREATE MULTISET TABLE adaboost_predict AS (
SELECT * FROM AdaBoostPredict (
ON iris_attribute_test AS attributetable PARTITION BY pid
ON iris_attribute_output AS model DIMENSION
USING
AttrTableGroupByColumns ('attribute')
AttrTablePIDColumns ('pid')
AttrTableValColumn ('attrvalue')
OutputResponseProbDist ('true')
Responses('1','2','3')
) AS dt
) WITH DATA;
Output
pid | pred_label | prob_for_label_1 | prob_for_label_2 | prob_for_label_3 -----+------------+------------------+------------------+------------------ 5 | 1 | 0.99888 | 0.00112 | 0.00000 10 | 1 | 0.98116 | 0.00062 | 0.01822 15 | 1 | 0.99888 | 0.00112 | 0.00000 20 | 1 | 0.99888 | 0.00112 | 0.00000 25 | 2 | 0.02431 | 0.92703 | 0.04865 30 | 1 | 0.98116 | 0.00062 | 0.01822 35 | 1 | 0.98116 | 0.00062 | 0.01822 40 | 1 | 1.00000 | 0.00000 | 0.00000 45 | 1 | 0.97561 | 0.02439 | 0.00000 50 | 1 | 1.00000 | 0.00000 | 0.00000 55 | 2 | 0.00965 | 0.93905 | 0.05131 60 | 2 | 0.01170 | 0.96589 | 0.02241 65 | 2 | 0.01145 | 0.96663 | 0.02192 70 | 2 | 0.01142 | 0.96671 | 0.02187 75 | 2 | 0.00907 | 0.94268 | 0.04825 80 | 2 | 0.01142 | 0.96671 | 0.02187 85 | 2 | 0.00965 | 0.93900 | 0.05134 90 | 2 | 0.00907 | 0.94268 | 0.04825 95 | 2 | 0.00907 | 0.94268 | 0.04825 100 | 2 | 0.00907 | 0.94268 | 0.04825 105 | 3 | 0.00000 | 0.01651 | 0.98349 110 | 3 | 0.00000 | 0.00764 | 0.99236 115 | 3 | 0.00000 | 0.01422 | 0.98578 120 | 2 | 0.01082 | 0.91434 | 0.07484 125 | 3 | 0.00000 | 0.00764 | 0.99236 130 | 2 | 0.01344 | 0.89826 | 0.08831 135 | 2 | 0.01242 | 0.90597 | 0.08161 140 | 3 | 0.00000 | 0.01651 | 0.98349 145 | 3 | 0.00000 | 0.00764 | 0.99236 150 | 3 | 0.00000 | 0.01790 | 0.98210 (30 rows)