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Applying CHAID for logistic regression diagnostics and classification accuracy improvement

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  • Antipov, Evgeny
  • Pokryshevskaya, Elena

Abstract

In this study a CHAID-based approach to detecting classification accuracy heterogeneity across segments of observations is proposed. This helps to solve some important problems, facing a model-builder: 1. How to automatically detect segments in which the model significantly underperforms? 2. How to incorporate the knowledge about classification accuracy heterogeneity across segments to partition observations in order to achieve better predictive accuracy? The approach was applied to churn data from the UCI Repository of Machine Learning Databases. By splitting the dataset into 4 parts, which are based on the decision tree, and building a separate logistic regression scoring model for each segment we increased the accuracy by more than 7 percentage points on the test sample. Significant increase in recall and precision was also observed. It was shown that different segments may have absolutely different churn predictors. Therefore such a partitioning gives a better insight into factors influencing customer behavior.

Suggested Citation

  • Antipov, Evgeny & Pokryshevskaya, Elena, 2009. "Applying CHAID for logistic regression diagnostics and classification accuracy improvement," MPRA Paper 21499, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:21499
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    References listed on IDEAS

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    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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    Cited by:

    1. Celal Hakan Kagnicioglu & Mune Mogol, 2014. "Implementation of Chaid Algorithm: A Hotel Case," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 3(4), pages 42-51, October.
    2. Wöcke, Albert & Moodley, Terence, 2015. "Corporate political strategy and liability of foreignness: Similarities and differences between local and foreign firms in the South African Health Sector," International Business Review, Elsevier, vol. 24(4), pages 700-709.

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    More about this item

    Keywords

    CHAID; logistic regression; churn prediction; performance improvement; segmentwise prediction; decision tree; classification tree;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C0 - Mathematical and Quantitative Methods - - General

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