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 21–30 of 537 papers

TitleStatusHype
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Axiomatic Attribution for Deep NetworksCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
A Unified Approach to Interpreting Model PredictionsCode1
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Benchmark Results

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