Antipov, Evgeny and Pokryshevskaya, Elena (2009): Applying CHAID for logistic regression diagnostics and classification accuracy improvement.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Applying CHAID for logistic regression diagnostics and classification accuracy improvement|
|English Title:||Applying CHAID for logistic regression diagnostics and classification accuracy improvement|
|Keywords:||CHAID; logistic regression; churn prediction; performance improvement; segmentwise prediction; decision tree; classification tree|
|Subjects:||M - Business Administration and Business Economics; Marketing; Accounting > M3 - Marketing and Advertising > M31 - Marketing
C - Mathematical and Quantitative Methods > C0 - General
|Depositing User:||Evgeny Antipov|
|Date Deposited:||29. Mar 2010 07:30|
|Last Modified:||11. Feb 2013 19:34|
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