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Why It Matters
In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. These explanations are often unreliable because they do not reflect the model's actual computations, limiting accountability and trust. A natural alternative is to use models that are interpretable by design. However, existing rule-based approaches, such as RuleFit and decision trees, while transparent, often lack stability and predictive strength, reinforcing a perceived trade-off between traditional performance measures and model understandabili...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.34019v1 · Indexed 42 minutes ago