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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

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Showing 251–260 of 537 papers

TitleStatusHype
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
Differentiable Genetic Programming for High-dimensional Symbolic Regression—0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing FlowsCode0
Verifying Properties of Tsetlin MachinesCode0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
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Benchmark Results

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