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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 211–220 of 537 papers

TitleStatusHype
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataCode0
An exact counterfactual-example-based approach to tree-ensemble models interpretabilityCode0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
Interpretable Machine Learning for Survival AnalysisCode0
LLM-based feature generation from text for interpretable machine learningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified