TD_OneClassSVM Example | OneClassSVM | Teradata Vantage - Example: How to Use TD_OneClassSVM - Teradata Vantage

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
ft:locale
en-US
ft:lastEdition
2024-12-11
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phg1621910019905.ditamap
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pny1626732985837.ditaval
dita:id
phg1621910019905

This is a sample of starting diabetes information.

ID Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
5 1 139 80 23 140 27.1 1.441 57
1 5 116 74 0 0 25.6 .0201 30
8 2 197 70 45 45 30.5 0.158 53
2 2 87 58 16 16 32.7 0.166 25
4 0 128 68 19 19 30.5 0.121 35
6 4 130 70 0 0 34.2 0.652 45
7 10 115 0 0 0 35.3 0.134 29
3 7 129 68 49 49 38.5 0.439 43
10 4 110 92 0 0 37.6 0.191 30
9 0 125 96 0 0 22.5 0.262 21

Example: TD_OneClassSVM Using LearningRate Constant

SELECT * FROM TD_OneClassSVM(
  ON diabetes_train_scaled
  USING
    InputColumns('[1:8]')
    Tolerance(1e-7)
    BatchSize(30)
    LearningRate('constant')
    InitialEta (0.01)
    RegularizationLambda(0.1)
    Alpha(0)
    Momentum (0.0)
    Nesterov ('false')
    MaxIterNum (100)
) AS dt

TD_OneClassSVM Output

Attribute Predictor Estimate Value
0 (Intercept) -0.002  
1 Pregnancies 0.0007820357  
2 Glucose -0.0009073822  
3 BloodPressure -0.0005117763  
4 SkinThickness -0.0001402478  
5 Insulin 0.0013394897  
6 BMI 0.0004039902  
7 DiabetesPedigreeFunction -0.0004613734  
8 Age -0.0011434892  
-1 Loss Function   HINGE
-2 Number of Observations 537  
-3 MSE -0.0001951275  
-4 AIC 18.0003902551  
-5 BIC 56.5743731056  
-6 Regularization 0.1 ENABLED
-7 Alpha 0 L2
-8 Number of Iterations 59 CONVERGED
-9 Learning Rate (Initial) 0.01  
-10 Learning Rate (Final) 0.01  
-11 Momentum 0  
-12 Nesterov   FALSE
-13 LocalSGD Iterations 0  
-15 Intercept Scaling 1  
-16 Sparse   FALSE
-17 Kernel   LINEAR
-18 OneClass SVM   TRUE