Parameters | Predictive Modeling | Standardization Method for Predictor Continuous Variables

Standardization is helpful when you want to ensure that all of the continuous predictor variables have the same locations and/or scales.Note: When you specify a Test Data Set or when you are running this process through Cross Validation Model Selection, standardization is performed only on the training data. The resulting location and scale statistics are used to standardize the test data.

Standardizes values to zero (0) with a scale equivalent to the sum of the values. Standardizes values to zero (0) with a scale equivalent to the Euclidean length. Standardizes values to zero (0) with a scale equivalent to the standard deviation about the origin. Standardizes values to zero (0) with a scale equivalent to the maximum absolute value. Standardizes values to the biweight 1-step M-estimate with a scale equivalent to the biweight A-estimate.Note: 4.685 is the default numeric tuning constant used in this method. Note: 1.345 is the default numeric tuning constant used in this method. Note: 1 is the default numeric tuning constant used in this method. Note: 0.1 is the default numeric constant giving the proportion of data to be contained in the spacing. Note: 0.1 is the default numeric constant specifying the power to which differences are to be raised in computing an L(p) or Minkowski metric.Refer to the SAS PROC STDIZE documentation for more information.