TD_OneClassSVM Usage Notes | OneClassSVM | Teradata Vantage - TD_OneClassSVM Usage Notes - Analytics Database

Database Analytic Functions

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Analytics Database
Release Number
17.20
Published
June 2022
Language
English (United States)
Last Update
2024-04-06
dita:mapPath
gjn1627595495337.ditamap
dita:ditavalPath
ayr1485454803741.ditaval
dita:id
jmh1512506877710
Product Category
Teradata Vantageā„¢
  • The categorical columns are converted to numerical columns as preprocessing step (for example, using TD_OneHotEncoding, TD_OrdinalEncoding, TD_TargetEncoding). TD_SVM takes all features as numeric input.
  • For a good model, standardize the dataset before feeding to TD_OneClassSVM as a preprocessing step (for example, using TD_Scale).
  • The rows with missing values are ignored during training and prediction of TD_OneClassSVM and TD_OneClassSVMPredict. Consider filling up those rows using imputation (TD_SimpleImpute) or other mechanism to train on rows with missing values.
  • The function supports linear SVMs only.
  • A maximum of 2046 features are supported due to the limitation imposed by the maximum number of columns (2048) in a database table for TD_OneClassSVM.