Input
- AttributeTable: iris_attribute_train
- ResponseTable: iris_response_train
The preceding tables are created in DecisionTree Example: Create Model.
SQL Call
DROP TABLE iris_attribute_output; SELECT * FROM DecisionTree ( ON iris_attribute_train AS AttributeTable ON iris_response_train AS ResponseTable OUT TABLE OutputTable (iris_attribute_output_prob) USING NumSplits (3) SplitMeasure ('gini') MaxDepth (10) IDColumns ('pid') AttributeNameColumns ('attribute') AttributeValueColumn ('attrvalue') ResponseColumn ('response') MinNodeSize (10) ApproxSplits ('false') OutputProb ('true') ) AS dt;
Output
message ----------------------------------------------------------------------------------------- Output model table is successfully stored in the table specified in OutputTable argument. Depth of the tree is:5
SELECT * FROM iris_attribute_output_prob;The table looks like iris_attribute_output in DecisionTree Example: Create Model, except that it has additional columns left_label_probdist, right_label_probdist, and prob_label_order.
node_id node_size node_gini_p node_entropy node_chisq_pv node_label node_majorvotes split_value split_gini_p split_entropy split_chisq_pv left_id left_size left_label left_majorvotes right_id right_size right_label right_majorvotes left_bucket right_bucket left_label_probdist right_label_probdist prob_label_order attribute ------- --------- ------------------- ------------------- ------------- ----------- --------------- ------------------ ------------------- ------------------- ---------------------- ------- --------- ----------- --------------- -------- ---------- ----------- ---------------- ----------- ------------ --------------------------------------- --------------------------------------- ----------------------------------------------------------- -------------- 2 90 0.5977777777777777 1.4900684346901674 1.0 2 40 1.7999999523162842 0.2459045406780598 0.6980234505326226 1.1102230246251565E-16 5 41 2 39 6 49 3 38 0.06522,0.02174,0.02174,0.02174,0.86957 0.72222,0.14815,0.01852,0.07407,0.03704 3, 5, 1, 4, 2 petal_width 5 41 0.09280190362879237 0.2811937964320433 1.0 2 39 5.0 0.08001711596063324 0.22991118255484383 0.01747194813144859 11 38 2 37 12 3 2 2 0.33333,0.16667,0.16667,0.16667,0.16667 0.33333,0.16667,0.16667,0.16667,0.16667 3, 5, 1, 4, 2 petal_length 11 38 0.05124653739612184 0.17556502585750336 1.0 2 37 5.5 0.04210526315789473 0.09499053880096872 0.009226867937210503 23 5 2 4 24 33 2 33 0.33333,0.16667,0.16667,0.16667,0.16667 0.20000,0.20000,0.20000,0.20000,0.20000 3, 5, 1, 4, 2 sepal_length 30 24 0.21875 0.5435644431995974 1.0 3 21 3.200000047683716 0.19999999999999996 0.4512050593046013 0.15149399240422035 61 15 3 12 62 9 3 9 0.65000,0.05000,0.05000,0.20000,0.05000 0.71429,0.07143,0.07143,0.07143,0.07143 3, 5, 1, 4, 2 sepal_width 0 130 0.7125443786982248 1.9220774796203766 1.0 1 40 1.0 0.4138461538461538 1.031585839400885 0.0 1 40 1 40 2 90 2 40 0.02222,0.02222,0.91111,0.02222,0.02222 0.43158,0.08421,0.01053,0.04211,0.43158 3, 5, 1, 4, 2 petal_width 6 49 0.3740108288213244 1.0467992835555564 1.0 3 38 6.300000190734863 0.2924397031539888 0.7112635941634022 1.4851330869192214E-4 13 16 3 8 14 33 3 30 0.45000,0.40000,0.05000,0.05000,0.05000 0.81579,0.02632,0.02632,0.10526,0.02632 3, 5, 1, 4, 2 sepal_length 14 33 0.1652892561983471 0.4394969869215134 1.0 3 30 2.9000000953674316 0.1590909090909091 0.3953195950542527 0.2659534093128423 29 9 3 9 30 24 3 21 0.71429,0.07143,0.07143,0.07143,0.07143 0.75862,0.03448,0.03448,0.13793,0.03448 3, 5, 1, 4, 2 sepal_width 13 16 0.5546875 1.2717822215997983 1.0 3 8 3.200000047683716 0.20833333333333331 0.5548650349110991 0.0019848295804182348 27 7 3 7 28 9 5 7 0.66667,0.08333,0.08333,0.08333,0.08333 0.15385,0.61538,0.07692,0.07692,0.07692 3, 5, 1, 4, 2 sepal_width
Download a zip file of all examples and a SQL script file that creates their input tables.