Comparison of methods of data mining techniques for the predictive accuracy.

Pyzhov, Vladislav and Pyzhov, Stanislav (2017): Comparison of methods of data mining techniques for the predictive accuracy.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Monte-Carlo, Data Mining, Neural Networks, k-nearest neighbors, Logistic regression, Random Forest.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software

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