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Business phase classification and prediction: how to compare interpretability of classification methods?

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  • Weihs, Claus
  • Sondhauss, Ursula

Abstract

When comparing methods for classification, often the rating relies on their prediction accuracy alone. One reason for this is that this is the aspect that can be most easily measured. Yet, often one wants to learn more about the problem than only how to predict. The interpretation of the relation of predictors and classes is often of high interest, but an unique accepted general formalization of interpretability relevant for many classification problems and measurable at least for a wide range of different classification methods does not exist, and - as we believe - is not really what is needed. Instead of trying to measure interpretability as such, standardizing and formalizing typical ways to interpret classification rules and finding performance criteria for this kind of outcomes leads to ratings of classification methods w.r.t. interpretability that can be tailored for the specific problem at hand and the subjective preferences of addressees of results. In this short paper, three results of this kind stemming from a comparative study of various classification methods applied to the classification of German business cycle phases based on 13 economic variables are exemplarily discussed .

Suggested Citation

  • Weihs, Claus & Sondhauss, Ursula, 2000. "Business phase classification and prediction: how to compare interpretability of classification methods?," Technical Reports 2000,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  • Handle: RePEc:zbw:sfb475:200024
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    File URL: https://www.econstor.eu/bitstream/10419/77309/2/2000-24.pdf
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    References listed on IDEAS

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    1. Sondhauss, Ursula & Weihs, Claus, 1999. "Dynamic Bayesian networks for classification of business cycles," Technical Reports 1999,17, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Heilemann, Ullrich & Münch, Heinz Josef, 2005. "The Clinton era and the U.S. business cycle : what did change?," Technical Reports 2005,12, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Heilemann, Ullrich & Münch, Heinz Josef, 2002. "Classifying US business cycles 1948 to 1997: Meyer/Weinberg revisited," Technical Reports 2002,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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