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 271–280 of 537 papers

TitleStatusHype
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Local Explanation of Dimensionality ReductionCode0
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
An interpretable machine learning approach for ferroalloys consumptions—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data—0
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Benchmark Results

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