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Measuring Predictor Importance

In: Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

    (Pfizer Global Research and Development, Division of Nonclinical Statistics)

  • Kjell Johnson

    (Arbor Analytics)

Abstract

Many predictive models have built-in or intrinsic measurements of predictor importance and have been discussed in previous chapters. For example, multivariate adaptive regression splines and many tree-based models monitor the increase in performance that occurs when adding each predictor to the model. Others, such as linear regression or logistic regression can use quantifications based on the model coefficients or statistical measures. The methodologies discussed in this chapter are not specific to any predictive model and can be used with numeric (Section 18.1) or categorical (Section 18.2) outcomes. Other modern importance algorithms such as Relief and MIC are presented in Section 18.3. In the Computing Section (18.4) we demonstrate how to implement these remedies in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "Measuring Predictor Importance," Springer Books, in: Applied Predictive Modeling, chapter 0, pages 463-485, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6849-3_18
    DOI: 10.1007/978-1-4614-6849-3_18
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