SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 890 papers

TitleStatusHype
Enhancing Osteoporosis Detection: An Explainable Multi-Modal Learning Framework with Feature Fusion and Variable Clustering—0
Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Ensembling improves stability and power of feature selection for deep learning models—0
Error-based Knockoffs Inference for Controlled Feature Selection—0
Error-controlled non-additive interaction discovery in machine learning models—0
Estimate the building height at a 10-meter resolution based on Sentinel data—0
Estimating oil recovery factor using machine learning: Applications of XGBoost classification—0
Estimating Sunlight Using GNSS Signal Strength from Smartphone—0
Evaluating graph-based explanations for AI-based recommender systems—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment—0
Evaluating the Correctness of Explainable AI Algorithms for Classification—0
Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru—0
Evaluation of Feature-based explanations—0
Evaluating Local Explanations using White-box Models—0
Examining Uniqueness and Permanence of the WAY EEG GAL dataset toward User Authentication—0
LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models—0
Explainability is NOT a Game—0
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing—0
Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Explainable AI without Interpretable Model—0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?—0
Shapley values for cluster importance: How clusters of the training data affect a prediction—0
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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