Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

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

Detecting outliers with the jackknife method


Outlier observations can have a dramatic (often negative) impact on the accuracy of many predictive models. Identifying Outlier observations and handling them appropriately is an important step in the data preparation phase of the CRISP-DM process. Outliers are often defined as observations with extreme values. This is a very limited criterion for defining an outlier. A more robust definition of outlier is an observation that contains a value that is significantly different from what would be predicted by a model built using the other observations in the sample. This definition is more robust as it allows observations to have extreme values if the model predicts an extreme value.

Extreme values can represent normal/predictable outcomes especially in cases of strong variable interaction.

The jackknife method is based on a Monte Carlo simulation where individual data points are held out of the training and testing partition. The overall accuracy of...