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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 231–240 of 537 papers

TitleStatusHype
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Interpretability with full complexity by constraining feature information—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Interpretability of machine learning based prediction models in healthcare—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Deducing neighborhoods of classes from a fitted model—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
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Benchmark Results

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