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 471–480 of 537 papers

TitleStatusHype
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Worth of knowledge in deep learningCode0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous DatasetsCode0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
From Human Explanation to Model Interpretability: A Framework Based on Weight of EvidenceCode0
Ontology-based Interpretable Machine Learning for Textual DataCode0
X Hacking: The Threat of Misguided AutoMLCode0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Show:102550
← PrevPage 48 of 54Next →

Benchmark Results

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