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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 371–380 of 537 papers

TitleStatusHype
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Interpretable Machine Learning for Kronecker Coefficients—0
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage—0
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Benchmark Results

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