SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–325 of 890 papers

TitleStatusHype
Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importanceCode0
Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories—0
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping—0
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing—0
BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning AlgorithmsCode0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
ShapG: new feature importance method based on the Shapley valueCode0
Explainability of Machine Learning Models under Missing DataCode0
Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methodsCode0
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI—0
The Impact of Feature Representation on the Accuracy of Photonic Neural NetworksCode0
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis—0
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study—0
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks—0
Multi-LLM QA with Embodied Exploration—0
FeatNavigator: Automatic Feature Augmentation on Tabular Data—0
Deep reinforcement learning with positional context for intraday trading—0
Learned Feature Importance Scores for Automated Feature Engineering—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous CatalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.76—Unverified
2VarImpVIANNPearson Correlation0.76—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.6—Unverified
2Garson Variable ImportancePearson Correlation0.22—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.86—Unverified
2Garson Variable ImportancePearson Correlation0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.83—Unverified
2Garson Variable ImportancePearson Correlation0.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.9—Unverified
2Garson Variable ImportancePearson Correlation0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.74—Unverified
2VarImpVIANNPearson Correlation0.41—Unverified