Book Image

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
Book Image

IBM SPSS Modeler Cookbook

By: Keith McCormick, Abbott

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (11 chapters)
10
Index

Using random imputation to match a variable's distribution

This recipe imputes missing values with actual values (selected at random) from the variable with missing values needing to be imputed. It is valuable when one does not want to impute with a constant but the variable has a distribution that isn't replicated well by a normal or uniform random imputation method.

In this recipe we will impute values for a missing or blank variable with a random value from the variable's own known values. This random imputation will therefore match the actual distribution of the variable itself.

Getting ready

This recipe uses the following files:

  • Datafile: cup98lrn_variable cleaning random impute recipe.sav
  • Stream file: Recipe – impute missing with actual values.str

How to do it...

  1. Open the stream (Recipe – impute missing with actual values.str) by navigating to File | Open Stream.
  2. Make sure the datafile points to the correct path and to the datafile (cup98lrn_variable cleaning...