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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 31–40 of 537 papers

TitleStatusHype
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
A Unified Approach to Interpreting Model PredictionsCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
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Benchmark Results

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