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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 537 papers

TitleStatusHype
A Unified Approach to Interpreting Model PredictionsCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Axiomatic Attribution for Deep NetworksCode1
Born-Again Tree EnsemblesCode1
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
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
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Benchmark Results

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