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 271–280 of 537 papers

TitleStatusHype
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Pest presence prediction using interpretable machine learning—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Show:102550
← PrevPage 28 of 54Next →

Benchmark Results

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