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 501–510 of 537 papers

TitleStatusHype
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry—0
Signed iterative random forests to identify enhancer-associated transcription factor bindingCode0
Interpretable Reinforcement Learning with Ensemble Methods—0
iNNvestigate neural networks!Code0
Knowledge Representation with Conceptual Spaces—0
Techniques for Interpretable Machine Learning—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems—0
Contrastive Explanations with Local Foil TreesCode0
Learning Kolmogorov Models for Binary Random Variables—0
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Benchmark Results

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