.png){kind=icon}
Return
StaTDS library: Statistical tests for Data Science
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2024.127877.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 opensource 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.
Keywords:
Statistical tests
Data science comparison
Python
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave no more
no more