Generalization of Scaled Tabular Models on Unseen High-Cardinality Features Across Benchmark Datasets

Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. In particular, the eigenvectors explain t

Research goal: How does the generalization of scaled tabular models trained on Criteo data perform on unseen high-cardinality categorical features in other benchmark datasets, as measured by AUC-ROC and precision-recall metrics?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.