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 241–250 of 537 papers

TitleStatusHype
Brain Age from the Electroencephalogram of Sleep—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Show:102550
← PrevPage 25 of 54Next →

Benchmark Results

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