StaTDS library: Statistical tests for Data Science

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作   者：

Christian Luna;  Antonio R. Moya;  Jose Maria Luna;  Sebastian Ventura; 

作者机构：

14071;  University of Cordoba;  Spain;  Department of Computing and Numerical Analysis;  Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI);  Cordoba; 

关键词：

Statistical tests;  Data science comparison;  Python; 

期刊名称：

Neurocomputing

i s s n：

0925-2312

年卷期：

2024 年 595 卷 Aug.28 期

页   码：

127877.1-127877.4

页   码：

摘   要：

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.