SOTAVerified

Feature Importance

Papers

Showing 326–350 of 890 papers

TitleStatusHype
I Bet You Did Not Mean That: Testing Semantic Importance via BettingCode0
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model—0
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for TransformersCode0
From SHAP Scores to Feature Importance Scores—0
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis—0
Analyze Additive and Interaction Effects via Collaborative Trees—0
Mitigating Text Toxicity with Counterfactual Generation—0
Feature Importance and Explainability in Quantum Machine LearningCode0
Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes—0
Estimate the building height at a 10-meter resolution based on Sentinel data—0
Feature importance to explain multimodal prediction models. A clinical use caseCode0
DTization: A New Method for Supervised Feature Scaling—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment—0
SIDEs: Separating Idealization from Deceptive Explanations in xAI—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deception—0
Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory—0
A Guide to Feature Importance Methods for Scientific InferenceCode0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data—0
Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients—0
CAGE: Causality-Aware Shapley Value for Global Explanations—0
Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models—0
Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision dataCode0
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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