[
    {
        "id": "osp-15259",
        "type": "article-journal",
        "title": "From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier",
        "author": [
            {
                "family": "Lemaire",
                "given": "Vincent"
            },
            {
                "family": "Clérot",
                "given": "Fabrice"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/from-log-odds-to-shapley-values-an-explanatory-geometry-for-the-weighted-naive-bayes-classifier",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the model. We start from the classical supervised distance based on conditional log-likelihoods and introduce a discriminative reformulation based on log-odds, which is more directly related to the classification decision. We then show that this representation induces a distance that exactly coincides with the $\\ell_1$ distance between vectors of analytical Shapley values, thereby providing a formal explanatory interpretation of the geometry induced by the model. Finally, we empirically compare several supervised distances derived from these representations using a $k$-nearest neighbors classifier. This work highlights a close link between supervised distance, local explanation, and predictive behavior, from a primarily methodological perspective."
    }
]