Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages

Tomasz Wiejak; Jakub Smołka

doi:10.35784/jcsi.6589

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Published: Dec 30, 2024

DOI: https://doi.org/10.35784/jcsi.6589

Issue Vol. 33 (2024)

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Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages
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DOI

https://doi.org/10.35784/jcsi.6589

Authors

Tomasz Wiejak

tomasz.wiejak@pollub.edu.pl

Lublin University of Technology, Poland

Jakub Smołka

j.smolka@pollub.pl

Lublin University of Technology, Poland

https://orcid.org/0000-0002-8350-2537

Abstract

The article compares machine learning tools using the example of several popular programming languages. Existing tools in the following programming languages were tested and compared with each other: Python, Java, R, Julia, C#. For the needs of article, algorithms were created in each studied language, operating on the same test set and using algorithms from the same group. The collected results included the program's running time, number of lines of code and accuracy of trained model. Based on the obtained data, conclusions were drawn that interpreted language libraries in terms of creating machine learning solutions are more effective than compiled language libraries.

Keywords:

machine learning, interpreted language, compiled language

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Wiejak, T., & Smołka, J. (2024). Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages. Journal of Computer Sciences Institute, 33, 339–345. https://doi.org/10.35784/jcsi.6589

Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages

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