Naive Bayes Classifiers and One-hot Encoding of Categorical Variables
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909183862898688 |
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| author | Williams, Christopher K. I. |
| author_facet | Williams, Christopher K. I. |
| contents | This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"ıve Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption, rather than the correct categorical Na\"ıve Bayes classifier. The differences between the two classifiers are analysed mathematically and experimentally. In our experiments using probability vectors drawn from a Dirichlet distribution, the two classifiers are found to agree on the maximum a posteriori class label for most cases, although the posterior probabilities are usually greater for the PoB case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Naive Bayes Classifiers and One-hot Encoding of Categorical Variables Williams, Christopher K. I. Machine Learning This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"ıve Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption, rather than the correct categorical Na\"ıve Bayes classifier. The differences between the two classifiers are analysed mathematically and experimentally. In our experiments using probability vectors drawn from a Dirichlet distribution, the two classifiers are found to agree on the maximum a posteriori class label for most cases, although the posterior probabilities are usually greater for the PoB case. |
| title | Naive Bayes Classifiers and One-hot Encoding of Categorical Variables |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.18190 |