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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 41–50 of 537 papers

TitleStatusHype
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
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Shapley variable importance clouds for interpretable machine learningCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Trees with Attention for Set Prediction TasksCode1
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Benchmark Results

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