Classification Model Using TrainingFunction with TD_XGBoost - Analytics Database

Database Analytic Functions
Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Analytics Database
Release Number
17.20
Published
June 2022
ft:locale
en-US
ft:lastEdition
2025-07-14
dita:mapPath
gjn1627595495337.ditamap
dita:ditavalPath
qkf1628213546010.ditaval
dita:id
jmh1512506877710
Product Category
Teradata Vantage™
Input Table
id sepal_length sepal_width petal_length petal_width species
1 5.10000000000000E 000 3.50000000000000E 000 1.40000000000000E 000 2.00E-01 1
2 4.90000000000000E 000 3.00000000000000E 000 1.40000000000000E 000 2.00E-01 1
3 4.70000000000000E 000 3.20000000000000E 000 1.30000000000000E 000 2.00E-01 1
4 4.60000000000000E 000 3.10000000000000E 000 1.50000000000000E 000 2.00E-01 1
5 5.00000000000000E 000 3.60000000000000E 000 1.40000000000000E 000 2.00E-01 1
6 5.40000000000000E 000 3.90000000000000E 000 1.70000000000000E 000 4.00E-01 1
7 4.60000000000000E 000 3.40000000000000E 000 1.40000000000000E 000 3.00E-01 1
8 5.00000000000000E 000 3.40000000000000E 000 1.50000000000000E 000 2.00E-01 1
9 4.40000000000000E 000 2.90000000000000E 000 1.40000000000000E 000 2.00E-01 1
10 4.90000000000000E 000 3.10000000000000E 000 1.50000000000000E 000 1.00E-01 1
... ... ... ... ... ...
Model Table
task_index tree_num iter class_num tree
0 -1 -1 -1 {"lossType":"SOFTMAX","numBoostedTrees":8,"iterNum":1,"classMapping":{"1":0,"2":1,"3":2}}
0 1 1 2 {"id_":1,"sum_":510.666667,"sumSq_":16047.111111,"size_":22,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.050000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":4193.454545,"scoreImprove_":4193.454545,"leftNodeSize_":16,"rightNodeSize_":6},"leftChild_":{"id_":2,"sum_":506.666667,"sumSq_":16044.444444,"size_":16,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.750000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":8,"rightNodeSize_":8},"leftChild_":{"id_":4,"sum_":253.333333,"sumSq_":8022.222222,"size_":8,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.950000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":2,"rightNodeSize_":6},"leftChild_":{"id_":8,"sum_":63.333333,"sumSq_":2005.555556,"size_":2,"maxDepth_":0,"value_":3
0 1 1 1 {"id_":1,"sum_":353911255.666666,"sumSq_":25197504836695924736.000000,"size_":22,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.600000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":4279076496322816512.000000,"scoreImprove_":4279076496322816512.000000,"leftNodeSize_":1,"rightNodeSize_":21},"leftChild_":{"id_":2,"sum_":-2004945786.333333,"sumSq_":4019807606135788032.000000,"size_":1,"maxDepth_":0,"value_":-2004945786.333333,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":3,"sum_":2358857042.000000,"sumSq_":21177697230560141312.000000,"size_":21,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.450000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":5972017308980156416.000000,"scoreImprove_":5972017308980156416.000000,"leftNodeSize_":4,"rightNodeSize_":17},"leftChild_":{"id_":6,"sum_":4846800936.666667,"sumSq_":8497445001240113152.000000,"size_":4,"maxDepth_
0 1 1 0 {"id_":1,"sum_":1.666667,"sumSq_":5.444444,"size_":22,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.150000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":5.318182,"scoreImprove_":5.318182,"leftNodeSize_":9,"rightNodeSize_":13},"leftChild_":{"id_":2,"sum_":6.000000,"sumSq_":4.000000,"size_":9,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.950000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":3,"rightNodeSize_":6},"leftChild_":{"id_":4,"sum_":2.000000,"sumSq_":1.333333,"size_":3,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":5,"sum_":4.000000,"sumSq_":2.666667,"size_":6,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"}},"rightChild_":{"id_":3,"sum_":-4.333333,"sumSq_":1.444444,"size_":13,"maxDepth_":0,"value_":-0.333333,"nodeType_":"REGRESSION_LEAF"}}
1 1 1 0 {"id_":1,"sum_":0.333333,"sumSq_":3.888889,"size_":17,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.650000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":3.882353,"scoreImprove_":3.882353,"leftNodeSize_":6,"rightNodeSize_":11},"leftChild_":{"id_":2,"sum_":4.000000,"sumSq_":2.666667,"size_":6,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.300000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":3,"rightNodeSize_":3},"leftChild_":{"id_":4,"sum_":2.000000,"sumSq_":1.333333,"size_":3,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":5,"sum_":2.000000,"sumSq_":1.333333,"size_":3,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"}},"rightChild_":{"id_":3,"sum_":-3.666667,"sumSq_":1.222222,"size_":11,"maxDepth_":0,"value_":-0.333333,"nodeType_":"REGRESSION_LEAF"}}
1 1 1 2 {"id_":1,"sum_":352.333333,"sumSq_":11033.222222,"size_":17,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.900000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":3730.941176,"scoreImprove_":3730.941176,"leftNodeSize_":11,"rightNodeSize_":6},"leftChild_":{"id_":2,"sum_":348.333333,"sumSq_":11030.555556,"size_":11,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.700000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":8,"rightNodeSize_":3},"leftChild_":{"id_":4,"sum_":253.333333,"sumSq_":8022.222222,"size_":8,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.000000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":2,"rightNodeSize_":6},"leftChild_":{"id_":8,"sum_":63.333333,"sumSq_":2005.555556,"size_":2,"maxDepth_":0,"value_":3
1 1 1 1 {"id_":1,"sum_":1475714351.333333,"sumSq_":17853048966241581056.000000,"size_":17,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.450000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":3700050645612942848.000000,"scoreImprove_":3700050645612942848.000000,"leftNodeSize_":5,"rightNodeSize_":12},"leftChild_":{"id_":2,"sum_":-3179690523.666666,"sumSq_":10553647499833659392.000000,"size_":5,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.000000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":6987097604131751936.000000,"scoreImprove_":6987097604131751936.000000,"leftNodeSize_":4,"rightNodeSize_":1},"leftChild_":{"id_":4,"sum_":-4908002423.333333,"sumSq_":7566585477304254464.000000,"size_":4,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.600000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":1527216767450153984.000000,"scoreI
2 1 1 2 {"id_":1,"sum_":416.333333,"sumSq_":13039.222222,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.650000,"attr_":"petal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":3480.192857,"scoreImprove_":3480.192857,"leftNodeSize_":14,"rightNodeSize_":6},"leftChild_":{"id_":2,"sum_":412.333333,"sumSq_":13036.555556,"size_":14,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.250000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":892.357143,"scoreImprove_":892.357143,"leftNodeSize_":1,"rightNodeSize_":13},"leftChild_":{"id_":4,"sum_":0.666667,"sumSq_":0.444444,"size_":1,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":5,"sum_":411.666667,"sumSq_":13036.111111,"size_":13,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":3.350000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.00000
2 1 1 1 {"id_":1,"sum_":7545668031.333336,"sumSq_":4066936141337179648.000000,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":3.350000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":281557116780666272.000000,"scoreImprove_":281557116780666272.000000,"leftNodeSize_":13,"rightNodeSize_":7},"leftChild_":{"id_":2,"sum_":3772834017.666666,"sumSq_":2033468070668589824.000000,"size_":13,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.650000,"attr_":"petal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":357532846705607936.000000,"scoreImprove_":357532846705607936.000000,"leftNodeSize_":9,"rightNodeSize_":4},"leftChild_":{"id_":4,"sum_":1616928867.000000,"sumSq_":871486316000824064.000000,"size_":9,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.950000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":331994786226635776.000000,"scoreImprove_":3
... ... ... ... ...
5 1 1 1 {"id_":1,"sum_":4311810306.000000,"sumSq_":2323963509335531008.000000,"size_":15,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.350000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":474475881982233472.000000,"scoreImprove_":474475881982233472.000000,"leftNodeSize_":5,"rightNodeSize_":10},"leftChild_":{"id_":2,"sum_":2694881438.333333,"sumSq_":1452477193334706944.000000,"size_":5,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.350000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":4,"rightNodeSize_":1},"leftChild_":{"id_":4,"sum_":2155905150.666667,"sumSq_":1161981754667765504.000000,"size_":4,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.150000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":3,"rightNodeSize_":1},"le
6 1 1 2 {"id_":1,"sum_":354.333333,"sumSq_":11034.555556,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.700000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":4756.950000,"scoreImprove_":4756.950000,"leftNodeSize_":11,"rightNodeSize_":9},"leftChild_":{"id_":2,"sum_":348.333333,"sumSq_":11030.555556,"size_":11,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.350000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":8,"rightNodeSize_":3},"leftChild_":{"id_":4,"sum_":253.333333,"sumSq_":8022.222222,"size_":8,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":4.750000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":2,"rightNodeSize_":6},"leftChild_":{"id_":8,"sum_":63.333333,"sumSq_":2005.555556,"size_":2,"maxDepth_":0,"value_":3
6 1 1 1 {"id_":1,"sum_":-1.666667,"sumSq_":3.888889,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.550000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":1.488095,"scoreImprove_":1.488095,"leftNodeSize_":6,"rightNodeSize_":14},"leftChild_":{"id_":2,"sum_":2.000000,"sumSq_":2.000000,"size_":6,"maxDepth_":0,"value_":0.333333,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":3,"sum_":-3.666667,"sumSq_":1.888889,"size_":14,"maxDepth_":0,"value_":-0.261905,"nodeType_":"REGRESSION_LEAF"}}
6 1 1 0 {"id_":1,"sum_":-0.666667,"sumSq_":4.222222,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.450000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":4.200000,"scoreImprove_":4.200000,"leftNodeSize_":6,"rightNodeSize_":14},"leftChild_":{"id_":2,"sum_":4.000000,"sumSq_":2.666667,"size_":6,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":3,"sum_":-4.666667,"sumSq_":1.555556,"size_":14,"maxDepth_":0,"value_":-0.333333,"nodeType_":"REGRESSION_LEAF"}}
7 1 1 1 {"id_":1,"sum_":7545668031.333335,"sumSq_":4066936141337179648.000000,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.750000,"attr_":"petal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":281557116780666656.000000,"scoreImprove_":281557116780666656.000000,"leftNodeSize_":13,"rightNodeSize_":7},"leftChild_":{"id_":2,"sum_":3772834017.666665,"sumSq_":2033468070668589824.000000,"size_":13,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.900000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":938523722602220800.000000,"scoreImprove_":938523722602220800.000000,"leftNodeSize_":7,"rightNodeSize_":6},"leftChild_":{"id_":4,"sum_":3772834013.666666,"sumSq_":2033468070668589824.000000,"size_":7,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.750000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNo
7 1 1 2 {"id_":1,"sum_":416.333333,"sumSq_":13039.222222,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":1.750000,"attr_":"petal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":4372.550000,"scoreImprove_":4372.550000,"leftNodeSize_":13,"rightNodeSize_":7},"leftChild_":{"id_":2,"sum_":411.666667,"sumSq_":13036.111111,"size_":13,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":3.250000,"attr_":"sepal_width","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":8,"rightNodeSize_":5},"leftChild_":{"id_":4,"sum_":253.333333,"sumSq_":8022.222222,"size_":8,"maxDepth_":3,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.150000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":2,"rightNodeSize_":6},"leftChild_":{"id_":8,"sum_":63.333333,"sumSq_":2005.555556,"size_":2,"maxDepth_":0,"value_":31.
7 1 1 0 {"id_":1,"sum_":0.333333,"sumSq_":4.555556,"size_":20,"maxDepth_":5,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":2.900000,"attr_":"petal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":4.550000,"scoreImprove_":4.550000,"leftNodeSize_":7,"rightNodeSize_":13},"leftChild_":{"id_":2,"sum_":4.666667,"sumSq_":3.111111,"size_":7,"maxDepth_":4,"nodeType_":"REGRESSION_NODE","split_":{"splitValue_":5.000000,"attr_":"sepal_length","type_":"REGRESSION_NUMERIC_SPLIT","score_":0.000000,"scoreImprove_":0.000000,"leftNodeSize_":3,"rightNodeSize_":4},"leftChild_":{"id_":4,"sum_":2.000000,"sumSq_":1.333333,"size_":3,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"},"rightChild_":{"id_":5,"sum_":2.666667,"sumSq_":1.777778,"size_":4,"maxDepth_":0,"value_":0.666667,"nodeType_":"REGRESSION_LEAF"}},"rightChild_":{"id_":3,"sum_":-4.333333,"sumSq_":1.444444,"size_":13,"maxDepth_":0,"value_":-0.333333,"nodeType_":"REGRESSION_LEAF"}}

TD_SHAP SQL Call Using TD_XGBoost

DROP TABLE iris_predict;
CREATE MULTISET TABLE iris_predict AS (
  SELECT * FROM TD_SHAP (
    ON iris_input  AS InputTable
    ON iris_model AS ModelTable DIMENSION
    OUT TABLE GlobalExplanation(shap_xgb_class_out)
    USING
    idcolumn('id')
    InputColumns ('[1:4]')
    modeltype('classification')
    trainingFunction('td_xgboost')
    NumParallelTrees(5)
    NumBoostRounds(1)
    detailed('1')
  ) AS dt
) WITH DATA;
shap_xgb_class_out Output Using TD_XGBoost
tree_num IterNum Label TD_sepal_length_SHAP TD_sepal_width_SHAP TD_petal_length_SHAP TD_petal_width_SHAP
01 1 0 1.53E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
01 1 1 1.50480831299513E 008 7.46910482102235E 007 5.50716619785201E 008 1.01145895530632E 008
01 1 2 8.81E-16 1.19E-15 1.24000000000000E 001 0.00000000000000E 000
11 1 0 1.85E-17 0.00000000000000E 000 1.50E-01 0.00000000000000E 000
11 1 1 3.84996856566540E 008 3.14393034807082E 007 3.72723775820163E 008 9.48018283159568E 007
11 1 2 8.99E-16 2.20E-16 1.40411764705883E 001 0.00000000000000E 000
21 1 0 0.00000000000000E 000 0.00000000000000E 000 1.50E-01 0.00000000000000E 000
21 1 1 7.05217859489031E 007 1.09354036635137E 008 6.42199824836310E 007 8.64033426786501E 007
21 1 2 1.39E-15 2.13014285714286E 000 0.00000000000000E 000 1.26701428571429E 001
31 1 0 0.00000000000000E 000 0.00000000000000E 000 1.48E-01 0.00000000000000E 000
31 1 1 1.92E-17 0.00000000000000E 000 1.00E-01 1.07E-01
31 1 2 3.05E-15 0.00000000000000E 000 0.00000000000000E 000 1.28362962962963E 001
41 1 0 1.38E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
41 1 1 1.01E-01 0.00000000000000E 000 1.06E-01 0.00000000000000E 000
41 1 2 1.85E-15 1.31E-15 1.45814814814815E 001 0.00000000000000E 000
FINAL 1 0 5.42E-02 0.00000000000000E 000 8.97E-02 0.00000000000000E 000
FINAL 1 1 9.56349356957458E 007 2.18928751669318E 007 1.59293785385713E 008 3.29549267347127E 007
FINAL 1 2 1.12E-15 4.26E-01 7.79954842543079E 000 5.05536190476190E 000

TD_SHAP SQL Call Using TD_XGBoost

drop table iris_predict;
CREATE MULTISET TABLE iris_predict AS (
  SELECT * FROM TD_SHAP (
    ON iris_input  AS InputTable
    ON iris_model AS ModelTable DIMENSION
    USING
    idcolumn('id')
    InputColumns ('[1:4]')
    modeltype('classification')
    trainingFunction('td_xgboost')
    NumParallelTrees(5)
    NumBoostRounds(1)
    detailed('0')
  ) AS dt
) WITH DATA;
TD_SHAP Output Table Using TD_XGBoost
Label Id TD_sepal_length_SHAP TD_sepal_width_SHAP TD_petal_length_SHAP TD_petal_width_SHAP
0 1 9.12E-02 0.00000000000000E 000 1.31E-01 0.00000000000000E 000
0 2 9.12E-02 0.00000000000000E 000 1.31E-01 0.00000000000000E 000
0 3 9.12E-02 0.00000000000000E 000 1.31E-01 0.00000000000000E 000
0 4 9.12E-02 0.00000000000000E 000 1.31E-01 0.00000000000000E 000
0 5 9.12E-02 0.00000000000000E 000 1.31E-01 0.00000000000000E 000
... ... ... ... ... ...
1 1 -3.07E-02 -4.66E-02 -4.44E-02 1.44E-02
1 2 -6.86E-02 -9.36E-03 -4.43E-02 1.48E-02
1 3 -6.86E-02 -9.36E-03 -4.43E-02 1.48E-02
1 4 -6.86E-02 -9.36E-03 -4.43E-02 1.48E-02
1 5 -3.07E-02 -4.66E-02 -4.44E-02 1.44E-02
... ... ... ... ... ...
2 1 -1.64E-15 3.76E-01 6.29025549613785E 000 3.51579365079365E 000
2 2 -1.73E-15 3.76E-01 6.29025549613785E 000 3.51579365079365E 000
2 3 -1.55E-15 3.76E-01 6.29025549613785E 000 3.51579365079365E 000
2 4 -1.20E-15 3.76E-01 6.29025549613785E 000 3.51579365079365E 000
2 5 -1.38E-15 3.76E-01 6.29025549613785E 000 3.51579365079365E 000
... ... ... ... ... ...

TD_SHAP SQL Call Using TD_XGBoost

DROP TABLE iris_predict;
CREATE MULTISET TABLE iris_predict AS (
SELECT * FROM TD_SHAP (
    ON iris_input  AS InputTable
    ON iris_model AS ModelTable DIMENSION
    OUT TABLE GlobalExplanation(shap_xgb_class_out_2)
    USING
    idcolumn('id')
    InputColumns ('[1:4]')
    modeltype('classification')
    trainingFunction('td_xgboost')
    NumParallelTrees(5)
    NumBoostRounds(1)
    detailed('false')
  ) AS dt
) WITH DATA;
Label TD_sepal_length_SHAP TD_sepal_width_SHAP TD_petal_length_SHAP TD_petal_width_SHAP
0 5.42E-02 0.00000000000000E 000 8.97E-02 0.00000000000000E 000
2 1.12E-15 4.26E-01 7.79954842543079E 000 5.05536190476190E 000
1 2.67E-02 3.35E-02 6.10E-02 3.24E-02

TD_SHAP SQL Call

DROP TABLE iris_predict;
CREATE MULTISET TABLE iris_predict AS (
SELECT * FROM TD_SHAP (
    ON iris_input  AS InputTable
    ON iris_model AS ModelTable DIMENSION
    USING
    idcolumn('id')
    InputColumns ('[1:4]')
    modeltype('classification')
    trainingFunction('td_xgboost')
    NumParallelTrees(5)
    NumBoostRounds(1)
    detailed('true')
  ) AS dt
) WITH DATA;
TD_SHAP Output
tree_num IterNum Label id TD_sepal_length_SHAP TD_sepal_width_SHAP TD_petal_length_SHAP TD_petal_width_SHAP
01 1 0 25 1.97E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
01 1 0 8 1.97E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
01 1 0 113 -1.36E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
01 1 0 1 1.97E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
01 1 0 148 -1.36E-01 0.00000000000000E 000 0.00000000000000E 000 0.00000000000000E 000
... ... ... ... ... ... ... ...
FINAL 1 2 108 -8.22E-16 1.55E-01 -1.23097445038622E 001 -8.66277777777778E 000
FINAL 1 2 15   3.76E-01 6.29025549613785E 000 3.51579365079365E 000
FINAL 1 2 139   1.55E-01 9.03E-02 -8.66277777777778E 000
FINAL 1 2 78   1.55E-01 -6.10974450386215E 000 -2.46277777777778E 000
FINAL 1 2 100 -6.22E-16 3.76E-01 6.29025549613785E 000 3.51579365079365E 000