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 231–240 of 537 papers

TitleStatusHype
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Insights into the origin of halo mass profiles from machine learning—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Interpretability of machine learning based prediction models in healthcare—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
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Benchmark Results

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