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 341–350 of 537 papers

TitleStatusHype
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Interpreting a Machine Learning Model for Detecting Gravitational Waves—0
REPID: Regional Effect Plots with implicit Interaction DetectionCode0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
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Benchmark Results

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