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Factors That Can Affect Model Performance

In: Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

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

  • Kjell Johnson

    (Arbor Analytics)

Abstract

Several of the preceding chapters have focused on technical pitfalls of predictive models, such as over-fitting and class imbalances. Often, true success may depend on aspects of the problem that are not directly related to the model itself. This chapter discusses topics such as: Type III errors (answering the wrong question, Section 20.1), the effect of unwanted noise in the response (Section 20.2) and in the predictors (Section 20.3), the impact of discretizing continuous outcomes (Section 20.4), extrapolation (Section 20.5), and the impact of a large number of samples (Section 20.6). In the Computing Section (20.7) we illustrate the implementation of an algorithm for determining samples’ similarity to the training set. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "Factors That Can Affect Model Performance," Springer Books, in: Applied Predictive Modeling, chapter 0, pages 521-546, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6849-3_20
    DOI: 10.1007/978-1-4614-6849-3_20
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