SOTAVerified

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 381–390 of 537 papers

TitleStatusHype
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Interpretable Machine Learning for Weather and Climate Prediction: A Survey—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
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
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Benchmark Results

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