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 191–200 of 537 papers

TitleStatusHype
Verifying Properties of Tsetlin MachinesCode0
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Causal Dependence Plots—0
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning modelCode0
Knowledge Discovery from Atomic Structures using Feature Importances—0
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitisCode1
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Benchmark Results

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