Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages
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 langu...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2024-12-01
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| Series: | Journal of Computer Sciences Institute |
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| Online Access: | https://ph.pollub.pl/index.php/jcsi/article/view/6589 |
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| _version_ | 1832570018621030400 |
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| author | Tomasz Wiejak Jakub Smołka |
| author_facet | Tomasz Wiejak Jakub Smołka |
| author_sort | Tomasz Wiejak |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-9bcba630856d4d9494c69e2530b8d249 |
| institution | Kabale University |
| issn | 2544-0764 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Journal of Computer Sciences Institute |
| spelling | doaj-art-9bcba630856d4d9494c69e2530b8d2492025-02-02T17:59:45ZengLublin University of TechnologyJournal of Computer Sciences Institute2544-07642024-12-013310.35784/jcsi.6589Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languagesTomasz Wiejak0Jakub Smołka1https://orcid.org/0000-0002-8350-2537Lublin University of TechnologyLublin University of Technology 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. https://ph.pollub.pl/index.php/jcsi/article/view/6589machine learninginterpreted languagecompiled language |
| spellingShingle | Tomasz Wiejak Jakub Smołka Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages Journal of Computer Sciences Institute machine learning interpreted language compiled language |
| title | Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages |
| title_full | Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages |
| title_fullStr | Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages |
| title_full_unstemmed | Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages |
| title_short | Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages |
| title_sort | performance of machine learning tools comparve analysis of libraries in interpreted and compiled programming languages |
| topic | machine learning interpreted language compiled language |
| url | https://ph.pollub.pl/index.php/jcsi/article/view/6589 |
| work_keys_str_mv | AT tomaszwiejak performanceofmachinelearningtoolscomparveanalysisoflibrariesininterpretedandcompiledprogramminglanguages AT jakubsmołka performanceofmachinelearningtoolscomparveanalysisoflibrariesininterpretedandcompiledprogramminglanguages |