We present the Deep Copula Classifier (DCC), a class-conditional generative model that separates marginal estimation from dependence modeling using neural copula densities. DCC is interpretable, Bayes-consistent, and achieves excess-risk $O(n^{-r/(2r+d)})$ for $r$-smooth copulas. In a controlled two-class study with strong dependence ($|\rho|=0.995$), DCC learns Bayes-aligned decision regions. With oracle or pooled marginals, it nearly reaches the best possible performance (accuracy $\approx 0.971$; ROC-AUC $\approx 0.998$). As expected, per-class KDE marginals perform less well (accuracy $0.873$; ROC-AUC $0.957$; PR-AUC $0.966$). On the Pima Indians Diabetes dataset, calibrated DCC ($\tau=1$) achieves accuracy $0.879$, ROC-AUC $0.936$, and PR-AUC $0.870$, outperforming Logistic Regression, SVM (RBF), and Naive Bayes, and matching Logistic Regression on the lowest Expected Calibration Error (ECE). Random Forest is also competitive (accuracy $0.892$; ROC-AUC $0.933$; PR-AUC $0.880$). Directly modeling feature dependence yields strong, well-calibrated performance with a clear probabilistic interpretation, making DCC a practical, theoretically grounded alternative to independence-based classifiers.

翻译：本文提出深度Copula分类器（DCC），这是一种基于神经Copula密度的类条件生成模型，其将边缘分布估计与相依性建模相分离。DCC具有可解释性、贝叶斯一致性，并对r阶光滑Copula函数达到$O(n^{-r/(2r+d)})$的过风险界。在具有强相依性（$|\rho|=0.995$）的受控二分类研究中，DCC能够学习到与贝叶斯准则对齐的决策区域。当使用真实边缘分布或合并边缘分布时，其性能近乎达到理论最优（准确率$\approx 0.971$；ROC-AUC $\approx 0.998$）。正如预期，基于核密度估计的各类别边缘分布表现稍逊（准确率$0.873$；ROC-AUC $0.957$；PR-AUC $0.966$）。在Pima印第安人糖尿病数据集上，经校准的DCC（$\tau=1$）取得准确率$0.879$、ROC-AUC $0.936$和PR-AUC $0.870$，其性能优于逻辑回归、支持向量机（RBF核）及朴素贝叶斯分类器，并在最低期望校准误差（ECE）指标上与逻辑回归相当。随机森林同样表现优异（准确率$0.892$；ROC-AUC $0.933$；PR-AUC $0.880$）。通过直接建模特征间的相依关系，DCC在保持清晰概率解释的同时实现了强大且校准良好的性能，这使其成为基于独立性假设分类器的一种实用且理论完备的替代方案。