TD_OneClassSVMPredict Example | OneClassSVMPredict - Example: How to Use TD_OneClassSVMPredict - Teradata Vantage

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
Language
English (United States)
Last Update
2024-04-03
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TD_OneClassSVMPredict Example: Outlier/Novelty Detection

The input is the diabetes_train_scaled set used in the example of TD_OneClassSVM.

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

TD_OneClassSVMPredict Model

attribute predictor estimate value
-3 Number of Observations 10.00000 None
-12 Nesterov NaN FALSE
-16 Kernel NaN LINEAR
0 (Intercept) 0.094000 None
5 Insulin 0.988265 None
-5 BIC 20.723266 None
-1 Loss Function NaN HINGE
-9 Learning Rate (Initial) 0.010000 None
3 BloodPressure 0.642372 None
-7 Alpha 0.000000 L2
7 DiabetesPedigreeFunction 0.003578 None
-17 OneClass SVM NaN TRUE
1 Pregnancies 0.033259 None
-13 LocalSGD Iterations 0.000000 None
-11 Momentum 0.000000 None
-2 Loglik -0.000000 None
-15 Intercept Scaling 1.000000 None
-8 Number of Iterations 52.00000 CONVERGED
-4 AIC 18.00000 None
6 BMI 0.298855 None
8 Age 0.349694 None
-10 Learning Rate (Final) 0.010000 None
4 SkinThickness .0144439 None
-6 Regularization 0.100000 ENABLED
2 Glucose 1.212525 None

TD_OneClassSVMPredict Call

CREATE multiset table prediction AS (
    SELECT * from TD_OneClassSVMPredict(
        ON diabetes_test_scaled AS InputTable PARTITION BY ANY
        ON oneclasssvm_model AS ModelTable DIMENSION
        USING
        IDColumn('id')
    ) AS dt
) WITH DATA;

TD_OneClassSVMPredict Test Data

ID Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
20 2 122 70 27 0 36.8 0.340 27
17 1 97 66 15 140 23.2 0.487 22
12 1 168 88 29 0 35.0 0.905 52
19 10 101 76 48 180 32.9 0.171 63
18 9 145 88 34 0 30.3 0.771 25
13 5 139 80 35 160 31.6 0.361 33
15 10 119 80 0 0 32.4 0.141 49
11 6 125 78 31 0 27.6 0.565 21
14 2 99 52 15 94 24.6 0.637 27
16 3 103 70 30 48 27.6 0.730  

TD_OneClassSVMPredict Output

ID prediction
20 1.0
17 1.0
12 1.0
19 1.0
18 1.0
13 1.0
15 1.0
11 1.0
14 1.0
16 1.0

Count normal as opposed to outlier.

SELECT CASE "prediction"
        WHEN 1 THEN 'normal'
        WHEN 0 THEN 'outlier'
        END SIGN,
        COUNT (*) counter
from prediction
GROUP BY "prediction"
SIGN counter
normal 10