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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 351–360 of 537 papers

TitleStatusHype
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
The Partial Response Network: a neural network nomogram—0
Model Bridging: Connection between Simulation Model and Neural Network—0
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
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Benchmark Results

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