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Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

Author

Listed:
  • Marcin Chlebus

    (Faculty of Economic Sciences, University of Warsaw)

  • Zuzanna Osika

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to use boosting algorithms, which have not been used before to predict the response for the campaign and to use Explainable AI (XAI) methods to evaluate model’s performance. The paper tries to examine whether 1) complex boosting algorithms perform better and 2) XAI tools are better indicators of models’ performance than commonly used discriminatory power’s measures like AUC. Portuguese bank telemarketing dataset was used with five machine learning algorithms, namely Random Forest (RF), AdaBoost, GBM, XGBoost and CatBoost, which were then later compared based on their AUC and XAI tools analysis – Permutated Variable Importance and Partial Dependency Profile. Two best performing models based on their AUC were XGBoost and CatBoost, with XGBoost having slightly higher AUC. Then, these models were examined using PDP and VI, which resulted in discovery of XGBoost potenitial overfitting and choosing CatBoost over XGBoost. The results show that new boosting models perform better than older models and that XAI tools could be helpful with models’ comparisons.

Suggested Citation

  • Marcin Chlebus & Zuzanna Osika, 2020. "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers 2020-15, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2020-15
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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5657/
    File Function: First version, 2020
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    References listed on IDEAS

    as
    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Srikrishna Chintalapati & Shivendra Kumar Pandey, 2025. "Factors driving the adoption of AI-powered marketing in financial services: a practitioner field study," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 52(1), pages 17-36, March.

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    More about this item

    Keywords

    direct marketing; telemarketing; relationship marketing; data mining; machine learning; random forest; adaboost; gbm; catboost; xgboost; bank marketing; XAI; variable importance; partial dependency profile;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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