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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 271–280 of 537 papers

TitleStatusHype
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
Interpretable machine learning in Physics—0
Interpretable Machine Learning in Physics: A Review—0
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation—0
Interpretable machine learning models: a physics-based view—0
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems—0
Interpretable Machine Learning Models for the Digital Clock Drawing Test—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development—0
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs—0
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Benchmark Results

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