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Impact of Data Normalization on Classification Model Accuracy

Author

Listed:
  • Borkin Dmitrii
  • Némethová Andrea
  • Michaľčonok German

    (Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Ulica Jána Bottu Č. 2781/25, 917 24 Trnava, Slovak Republic)

  • Maiorov Konstantin

    (Kalashnikov Izhevsk State Technical University, Department of Computer Software, 4260069 Izhevsk, Ul. Studenčeskaja 7, Russian Federation)

Abstract

In this paper, we present the impact of the data normalization on the classification model performance. In first part of this paper, we present the structure of our dataset, where we discuss the features of the data set and basic statistical analysis of the data. In this research, we worked with the medical data about the patients with the Parkinson disease. In second part of this paper, we present the process of data normalization and the impact of scaling data on the classification model performance. In this research, we used the XGBoost model as our classification model. The main classification task was to classify whether the patient is ill with Parkinson disease or not. Since the data set contains more numerical parameters of different scaling, the main aim of this paper was to investigate the impact of the data normalization (scaling) on the performance of the classification model.

Suggested Citation

  • Borkin Dmitrii & Némethová Andrea & Michaľčonok German & Maiorov Konstantin, 2019. "Impact of Data Normalization on Classification Model Accuracy," Research Papers Faculty of Materials Science and Technology Slovak University of Technology, Sciendo, vol. 27(45), pages 79-84, September.
    • Handle: RePEc:vrs:repfms:v:27:y:2019:i:45:p:79-84:n:11
    DOI: 10.2478/rput-2019-0029
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