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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 301–310 of 537 papers

TitleStatusHype
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Designing Inherently Interpretable Machine Learning ModelsCode2
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Benchmark Results

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