arXiv:2607. 14096v1 Announce Type: new Abstract: In predictive modeling, the ability to explain why a model produces a given target prediction has become increasingly important [5, 10].
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly.
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables.
arXiv:2603. 13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision.
arXiv:2606. 14245v1 Announce Type: new Abstract: Drug-target interaction (DTI) and affinity (DTA) predictors increasingly achieve strong benchmark scores, yet their internal use of sequence, fingerprint, and graph features often remains opaque.
arXiv:2512. 11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models.
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.