XGBoost Example: Regression, Sparse Format | Teradata Vantage - XGBoostPredict Example: Regression, Sparse Format - Teradata Vantage

Input

As in XGBoost Example: Regression, Sparse Format:

• InputTable: boston_sparse
• AttributeTable: sparse_boston_attributes

SQL Call

CREATE MULTISET TABLE housing_predict AS (
  SELECT * FROM XGBoostPredict (
    ON boston_sparse AS InputTable partition by id
    ON xgboost_regression_model AS Model dimension order by tree_id, iter, class_num
    USING
    AttributeNameColumn ('attribute')
    AttributeValueColumn ('value1')
    IDColumn ('id')
    Accumulate ('medv')
    NumBoostedTrees ('1')
  ) AS dt
) WITH DATA;

Output

id  medv prediction         confidence_lower       confidence_upper
--- ---- ------------------ ---------------------- ---------------------
469   19 9.581591896484374  -1.58027885235156E 001 3.49659723164844E 001
265   36 24.127591576484374  2.10018126236844E 001 2.72533705292844E 001
 40   31 34.80977439648437   1.07469171164844E 001 5.88726316764844E 001
122   20 26.177407008484376  1.90339898089644E 001 3.33208242080044E 001
 61   19 20.134487347817707  1.54337820124310E 001 2.48351926832044E 001
244   24 22.994653131684373  2.20894335306924E 001 2.38998727326764E 001
162   50 22.490095176484374  2.24063811852844E 001 2.25738091676844E 001
387   10 5.195647863151041  -2.87851828621823E 001 3.91764785884844E 001
326   25 22.70793660115104   2.23646814000044E 001 2.30511918022977E 001
305   36 34.91974709648438   1.06413433244844E 001 5.91981508684844E 001
223   28 23.949344576484375  2.11729297436844E 001 2.67257594092844E 001
448   13 16.687310416484372  5.23013829568437E 000 2.81444825372844E 001
183   38 35.40923309648437   1.01714367644844E 001 6.06470294284844E 001
101   28 23.949344576484375  2.11729297436844E 001 2.67257594092844E 001
488   21 21.67857907094801   2.00042935128967E 001 2.33528646289993E 001
 19   20 16.053501116484377  3.35406276768438E 000 2.87529394652844E 001
366   28 27.337826516484377  1.79199870812844E 001 3.67556659516844E 001
427   10 16.687310416484372  5.23013829568437E 000 2.81444825372844E 001