StaTDS library: Statistical tests for Data Science

In Data Science, there is a continual demand for statistical comparison to identify the most advantageous algorithms. Finding a software tool that facilitates the execution of multiple tests on different Data Science experiments without relying on additional libraries poses a challenge. This paper i...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 595; p. 127877
Main Authors Luna, Christian, Moya, Antonio R., Luna, José María, Ventura, Sebastián
Format Journal Article
LanguageEnglish
Published Elsevier B.V 28.08.2024
Subjects
Data science comparison
Python
Statistical tests
Statistical tests
Data science comparison
Python
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ISSN0925-2312
1872-8286
1872-8286
DOI10.1016/j.neucom.2024.127877

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Summary:In Data Science, there is a continual demand for statistical comparison to identify the most advantageous algorithms. Finding a software tool that facilitates the execution of multiple tests on different Data Science experiments without relying on additional libraries poses a challenge. This paper introduces StaTDS, an open-source library and web application implemented entirely in pure Python, designed to analyze, test, and compare Data Science algorithms. StaTDS implements all statistical tests without external dependencies. It ensures its durability and avoids future uncontrolled deprecated dependencies. With support for a wide variety of statistical tests (24 in total), StaTDS surpasses existing libraries dedicated to statistical testing. Moreover, the library incorporates tests to guide users in determining whether to employ parametric or non-parametric tests, such as the assessment of normality and homoscedasticity. This platform-independent library is available on GitHub under the GNU General Public License.
ISSN:0925-2312
1872-8286
1872-8286
DOI:10.1016/j.neucom.2024.127877