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

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
Language
English (United States)
Last Update
2024-04-03
dita:mapPath
phg1621910019905.ditamap
dita:ditavalPath
pny1626732985837.ditaval
dita:id
phg1621910019905
  • 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/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.