OPTIMAL BINNING

OPTIMAL BINNING is available in the Data Preparation option.

The OPTIMAL BINNING procedure discretizes one or more scale variables by distributing the values of each variable into bins. Bins can then be used instead of the original data values of the binning input variables for further analysis. OPTIMAL BINNING is useful for reducing the number of distinct values in the given binning input variables.

OPTIMAL BINNING

/VARIABLES [GUIDE = variable] BIN = varlist [SAVE = {NO**                       }]
                                                    {YES [(INTO = new varlist)]}

[/CRITERIA

    [PREPROCESS = {EQUALFREQ**[(BINS = {1000**})]}]
                                      {n     }
                  {NONE                          }

    [METHOD = {MDLP**                     }]
              {EQUALFREQ [(BINS = {10**})]}
                                  {n   }

    [LOWEREND = {UNBOUNDED**}]     [UPPEREND = {UNBOUNDED**}]
                {OBSERVED   }                  {OBSERVED   }


    [LOWERLIMIT = {INCLUSIVE**}]
                  {EXCLUSIVE  }

    [FORCEMERGE = {0**  }]]
                  {value}

[/MISSING  [SCOPE = {PAIRWISE**}]]
                    {LISTWISE  }

[/OUTFILE  RULES = filespec]

[/PRINT  [ENDPOINTS**] [DESCRIPTIVES] [ENTROPY] [NONE]]

** Default if the subcommand or keyword is omitted.

This command reads the active dataset and causes execution of any pending commands. See the topic Command Order for more information.

Syntax for the OPTIMAL BINNING command can be generated from the Optimal Binning dialog.

Release History

Release 15.0

• Command introduced.

Example

OPTIMAL BINNING
  /VARIABLES GUIDE = guide-variable BIN = binning-input-variable