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 291–300 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
POTATO: exPlainable infOrmation exTrAcTion framewOrkCode1
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
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
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
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Benchmark Results

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