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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 181–190 of 537 papers

TitleStatusHype
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
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
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Benchmark Results

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