1.1 - 8.10 - SVMDense Example: Sigmoid Model - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Release Date
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)

SQL Call

DROP TABLE densesvm_iris_sigmoid_model;

SELECT * FROM SVMDense (
  ON svm_iris_train AS InputTable
  OUT TABLE ModelTable (densesvm_iris_sigmoid_model)
  USING
  IDColumn ('id')
  TargetColumns ('[1:4]')
  ResponseColumn ('species')
  RegularizationLambda (1)
  Bias (0)
  KernelFunction ('sigmoid')
  Gamma (0.1)
  HashBits (512)
  SubspaceDimension (120)
  MaxIterNum (30)
  Seed (1)
) AS dt;

Output

 message                                                                                                                                   
 ----------------------------------------------------------------------------------------------------------------------------------------- 
 Model table is created successfully                                                                                                      
 The model is trained with 120 samples and 512 unique attributes with hash projection                                                     
 There are 3 different classes in the training set                                                                                        
 The model is converged after 12 steps with epsilon 0.01, the average value of the loss function for the training set is 56.53662594767713
 The corresponding training parameters are cost:1.0 bias:0.0

Only models with RBF and Sigmoid kernels have converged. This may mean that the true boundaries in the data set are hard to capture with a linear or polynomial model.

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