Package smile.feature.transform

Class Scaler

java.lang.Object
smile.feature.transform.Scaler
public class Scaler extends Object
Scales the numeric variables into the range [0, 1]. If the dataset has outliers, normalization will certainly scale the "normal" data to a very small interval. In this case, the Winsorization procedure should be applied: values greater than the specified upper limit are replaced with the upper limit, and those below the lower limit are replaced with the lower limit. Often, the specified range is indicate in terms of percentiles of the original distribution (like the 5th and 95th percentile).

  • Constructor Details

    • Scaler

      public Scaler()

  • Method Details

    • fit

      public static InvertibleColumnTransform fit(DataFrame data, String... columns)
      Fits the data transformation.
      Parameters:
      data - the training data.
      columns - the columns to transform. If empty, transform all the numeric columns.
      Returns:
      the transform.