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Comparison of methods of data mining techniques for the predictive accuracy

Author

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  • Pyzhov, Vladislav
  • Pyzhov, Stanislav

Abstract

This paper is based on the work of Yeh, Lien (2009). In the paper, authors used the payment data set from the important bank in Taiwan. To build a model, the whole sample was divided in two subsets - training and testing sets - so each model could be trained on the first one and then be evaluated on the second. Our motivation was to see whether the same result could be obtained if we repeatedly apply the models to the different data sets. To do so, Monte Carlo simulation was implemented to generate these sets.

Suggested Citation

  • Pyzhov, Vladislav & Pyzhov, Stanislav, 2017. "Comparison of methods of data mining techniques for the predictive accuracy," MPRA Paper 79326, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:79326
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    File URL: https://mpra.ub.uni-muenchen.de/79326/1/MPRA_paper_79326.pdf
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    References listed on IDEAS

    as
    1. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
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    More about this item

    Keywords

    Monte-Carlo; Data Mining; Neural Networks; k-nearest neighbors; Logistic regression; Random Forest.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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