1.1 - 8.10 - Normalized Input - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
October 2019
Content Type
Programming Reference
Publication ID
English (United States)
Last Update

For some predictive modeling functions, it is very important to normalize the numeric input variables; that is, to rescale them so they have a similar mean and standard deviation. If you do not normalize input variables, the effect of variables with a large magnitude or a large standard deviation may dominate the model and reduce the accuracy of its predictions.

Normalize input variables before calling the following functions:
  • Canopy
  • KMeans Functions
    • KMeans
    • KMeansPredict
  • KNN
  • Generalized Linear Model (GLM) Functions
    • GLM
    • GLMPredict_MLE
    • GLML1L2
    • GLML1L2Predict
  • Least Angle Regression (LAR) Functions
    • LAR
    • LARPredict
  • Linear Regression Functions
    • Linear Regression
    • LinRegPredict
  • Principal Component Analysis (PCA) Functions
    • PCA
    • PCAScore
  • Support Vector Machine (SVM) Functions
    • SVMSparse
    • SVMSparsePredict_MLE
    • SVMSparseSummary
    • SVMDense
    • SVMDensePredict
    • SVMDenseSummary

The MLE Scale functions are designed to make normalization easy. For an example of using Scale functions to normalize input variables, see PCA Example.