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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 381–390 of 537 papers

TitleStatusHype
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models—0
A hybrid machine learning framework for analyzing human decision making through learning preferences—0
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency—0
On Explaining Decision Trees—0
On Interpretability and Similarity in Concept-Based Machine Learning—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
Online Product Feature Recommendations with Interpretable Machine Learning—0
On quantitative aspects of model interpretability—0
On the definition and importance of interpretability in scientific machine learning—0
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Benchmark Results

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