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 Neural Network for Feature Selection


When building a predictive model, there may be a large number of data fields available for use as inputs to the model. Selecting only those fields most useful to the model has a variety of advantages; it simplifies the model-building process, leading to better and simpler models, and it simplifies the resulting models, leading to more effective insight and easier Deployment.

This Feature Selection can be achieved through a variety of techniques, business and data knowledge can be applied to select the fields likely to be relevant, and univariate techniques can be used to select individual fields that have a relation to the predictive target. It is also a common practice to use other models to help select features whose relevance is more multivariate in nature. Decision trees are often used for this purpose, because building a decision tree model implicitly selects relevant variables; each variable is either used in the model, therefore indicated...