DecisionForest Example: TreeType ('regression'), OutOfBag ('true') - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
8.10
1.1
Published
October 2019
Language
English (United States)
Last Update
2019-12-31
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ima1540829771750.ditamap
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dita:id
B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢

This example uses the same housing data set as the previous examples. Instead of trying to predict the style of the house, this example uses homestyle as a predictor and tries to predict the price of the house. Because the response variable (price) is numeric, the TreeType is regression.

SQL Call

SELECT * FROM DecisionForest (
  ON housing_train AS InputTable
  OUT TABLE OutputTable (df_model)
  OUT TABLE OutputMessageTable (df_monitor_table)
  USING
  TreeType ('regression')
  ResponseColumn ('price') 
  NumericInputs ('lotsize','bedrooms','bathrms','stories','garagepl')
  CategoricalInputs ('homestyle','driveway','recroom','fullbase','gashw','airco','prefarea')
  MaxDepth (6) 
  MinNodeSize (2)
  NumTrees (50)
  OutOfBag ('true') 
) AS dt;

Output

 message                                          
 ------------------------------------------------ 
 Computing 50 regression trees.                  
 Each worker is computing 25 trees.              
 Each tree will contain approximately 246 points.
 Poisson sampling parameter: 1.00                
 Mean of squared residuals: 1.1802873865165223E8   
  % Var explained: 97.78732397804998             
 Decision forest created.

This query returns the following table:

SELECT task_index, tree_num, CAST (tree AS VARCHAR(50))
  FROM df_model ORDER BY 1;
task_index tree_num tree                                               
 ---------- -------- -------------------------------------------------- 
          4        0 {"sum_":1.681685E7,"sumSq_":1.4238502825E12,"size_
          4        1 {"sum_":1.727905E7,"sumSq_":1.3643563875E12,"size_
          4        2 {"sum_":1.694355E7,"sumSq_":1.3956032975E12,"size_
          4        3 {"sum_":1.908355E7,"sumSq_":1.4617931525E12,"size_
          4        4 {"sum_":1.806E7,"sumSq_":1.474040925E12,"size_":25
          4        5 {"sum_":1.63545E7,"sumSq_":1.320355595E12,"size_":
          4        6 {"sum_":1.862795E7,"sumSq_":1.4897402375E12,"size_
          4        7 {"sum_":1.759165E7,"sumSq_":1.3890647825E12,"size_
          4        8 {"sum_":1.558195E7,"sumSq_":1.2663727725E12,"size_
          4        9 {"sum_":1.537575E7,"sumSq_":1.2086245025E12,"size_
          4       10 {"sum_":1.581315E7,"sumSq_":1.1909563425E12,"size_
          4       11 {"sum_":1.75272E7,"sumSq_":1.341120065E12,"size_":
          4       12 {"sum_":1.673795E7,"sumSq_":1.3203427375E12,"size_
          4       13 {"sum_":1.703575E7,"sumSq_":1.3940797775E12,"size_
          4       14 {"sum_":1.507785E7,"sumSq_":1.1982603775E12,"size_
          4       15 {"sum_":1.538635E7,"sumSq_":1.2125508275E12,"size_
          4       16 {"sum_":1.56961E7,"sumSq_":1.259207185E12,"size_":
          4       17 {"sum_":1.892375E7,"sumSq_":1.4711988825E12,"size_
          4       18 {"sum_":1.57514E7,"sumSq_":1.28476287E12,"size_":2
          4       19 {"sum_":1.62982E7,"sumSq_":1.305691615E12,"size_":
          4       20 {"sum_":1.56613E7,"sumSq_":1.193288085E12,"size_":
          4       21 {"sum_":1.719835E7,"sumSq_":1.3850259525E12,"size_
          4       22 {"sum_":1.72148E7,"sumSq_":1.361429395E12,"size_":
          4       23 {"sum_":1.558585E7,"sumSq_":1.2253462225E12,"size_
          4       24 {"sum_":1.713935E7,"sumSq_":1.3091327925E12,"size_
          5        0 {"sum_":1.499364E7,"sumSq_":1.09838566255E12,"size
          5        1 {"sum_":1.621284E7,"sumSq_":1.21153072755E12,"size
          5        2 {"sum_":1.584329E7,"sumSq_":1.20964782505E12,"size
          5        3 {"sum_":1.8707295E7,"sumSq_":1.446280402525E12,"si
          5        4 {"sum_":1.66777E7,"sumSq_":1.261864825E12,"size_":
          5        5 {"sum_":1.641145E7,"sumSq_":1.3340593525E12,"size_
          5        6 {"sum_":1.791324E7,"sumSq_":1.35688597255E12,"size
          5        7 {"sum_":1.57669E7,"sumSq_":1.177536645E12,"size_":
          5        8 {"sum_":1.4742645E7,"sumSq_":1.112824970025E12,"si
          5        9 {"sum_":1.548094E7,"sumSq_":1.19681165255E12,"size
          5       10 {"sum_":1.6602745E7,"sumSq_":1.345563175025E12,"si
          5       11 {"sum_":1.696595E7,"sumSq_":1.2466159725E12,"size_
          5       12 {"sum_":1.6895545E7,"sumSq_":1.356572220025E12,"si
          5       13 {"sum_":1.630075E7,"sumSq_":1.2469239875E12,"size_
          5       14 {"sum_":1.43426E7,"sumSq_":1.07212618E12,"size_":2
          5       15 {"sum_":1.4557895E7,"sumSq_":1.101687512525E12,"si
          5       16 {"sum_":1.4730795E7,"sumSq_":1.099434292525E12,"si
          5       17 {"sum_":1.824529E7,"sumSq_":1.39450218005E12,"size
          5       18 {"sum_":1.510695E7,"sumSq_":1.1471032125E12,"size_
          5       19 {"sum_":1.5264045E7,"sumSq_":1.125377830025E12,"si
          5       20 {"sum_":1.6693645E7,"sumSq_":1.395296370025E12,"si
          5       21 {"sum_":1.5762345E7,"sumSq_":1.110753800025E12,"si
          5       22 {"sum_":1.748599E7,"sumSq_":1.34602377005E12,"size
          5       23 {"sum_":1.5813695E7,"sumSq_":1.199732752525E12,"si
          5       24 {"sum_":1.6980695E7,"sumSq_":1.333814232525E12,"si

Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.