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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
4.4 (20)
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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

4.4 (20)
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)
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10
Index

Using the Feature Selection node creatively to remove or decapitate perfect predictors

In this recipe, we will identify perfect or near perfect predictors in order to insure that they do not contaminate our model. Perfect predictors earn their name by being correct 100 percent of the time, usually indicating circular logic and not a prediction of value. It is a common and serious problem.

When this occurs we have accidentally allowed information into the model that could not possibly be known at the time of the prediction. Everyone 30 days late on their mortgage receives a late letter, but receiving a late letter is not a good predictor of lateness because their lateness caused the letter, not the other way around.

The rather colorful term decapitate is borrowed from the data miner Dorian Pyle. It is a reference to the fact that perfect predictors will be found at the top of any list of key drivers ("caput" means head in Latin). Therefore, to decapitate is to remove the variable...

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