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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 281–290 of 537 papers

TitleStatusHype
Dynamic Model Tree for Interpretable Data Stream LearningCode0
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems—0
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
GAM(L)A: An econometric model for interpretable Machine Learning—0
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Interpretable machine learning in Physics—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
Toward More Generalized Malicious URL Detection Models—0
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Benchmark Results

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