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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 321–330 of 537 papers

TitleStatusHype
Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients—0
Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Is it Fake? News Disinformation Detection on South African News WebsitesCode0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behaviorCode0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataCode0
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
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Benchmark Results

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