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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
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Born-Again Tree EnsemblesCode1
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
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
Axiomatic Attribution for Deep NetworksCode1
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Benchmark Results

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