In machine learning, the Bayes classifier represents the theoretical optimum for minimizing classifica-tion errors. Since estimating high-dimensional probability densities is impractical, simplified approxima-tions such as naïve Bayes and k-nearest neighbor are widely used as baseline classifiers. Despite their simplicity, these methods require design choices—such as the distance measures in kNN, or the feature independence in naïve Bayes. In particular, naïve Bayes relies on implicit assumptions by using Gaussi-an mixtures or univariate kernel density estimators. Such design choices, however, often fail to capture heterogeneous distributional structures across features. We propose a flexible naïve Bayes classifier that leverages Pareto Density Estimation (PDE), a parame-ter-free, non-parametric approach shown to outperform standard kernel methods in exploratory statis-tics (Thrun et al., 2020). PDE avoids prior distributional assumptions and supports interpretability through visualization of class-conditional likelihoods. In addition, we address a recently described pit-fall of Bayes’ theorem: the misclassification of observations with low evidence. Building on the con-cept of plausible Bayes (Ultsch Lötsch, 2022), we introduce a safeguard to handle uncertain cases more reliably. While not aiming to surpass state-of-the-art classifiers, our results show that PDE-flexible naïve Bayes with uncertainty handling provides a robust, scalable, and interpretable baseline that can be applied across diverse data scenarios.
Stier et al. (Fri,) studied this question.
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