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 31–40 of 537 papers

TitleStatusHype
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predicting and Understanding College Student Mental Health with Interpretable Machine LearningCode0
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change—0
Diagnostic-free onboard battery health assessment—0
A Frank System for Co-Evolutionary Hybrid Decision-Making—0
Near Optimal Decision Trees in a SPLIT Second—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning—0
Interpretable Machine Learning for Kronecker Coefficients—0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
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Benchmark Results

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