gcor: A Python Implementation of Categorical Gini Correlation and Its Inference
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915999390892032 |
|---|---|
| author | Hewage, Sameera |
| author_facet | Hewage, Sameera |
| contents | Categorical Gini Correlation (CGC), introduced by Dang et al. (2020), is a novel dependence measure designed to quantify the association between a numerical variable and a categorical variable. It has appealing properties compared to existing dependence measures, such as zero correlation mutually implying independence between the variables. It has also shown superior performance over existing methods when applied to feature screening for classification. This article presents a Python implementation for computing CGC, constructing confidence intervals, and performing independence tests based on it. Efficient algorithms have been implemented for all procedures, and they have been optimized using vectorization and parallelization to enhance computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | gcor: A Python Implementation of Categorical Gini Correlation and Its Inference Hewage, Sameera Methodology Computation 62H20, 62G20, 62-07, 62R07 Categorical Gini Correlation (CGC), introduced by Dang et al. (2020), is a novel dependence measure designed to quantify the association between a numerical variable and a categorical variable. It has appealing properties compared to existing dependence measures, such as zero correlation mutually implying independence between the variables. It has also shown superior performance over existing methods when applied to feature screening for classification. This article presents a Python implementation for computing CGC, constructing confidence intervals, and performing independence tests based on it. Efficient algorithms have been implemented for all procedures, and they have been optimized using vectorization and parallelization to enhance computational efficiency. |
| title | gcor: A Python Implementation of Categorical Gini Correlation and Its Inference |
| topic | Methodology Computation 62H20, 62G20, 62-07, 62R07 |
| url | https://arxiv.org/abs/2506.19230 |