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Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 890 papers

TitleStatusHype
Estimating Sunlight Using GNSS Signal Strength from Smartphone—0
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation—0
Estimating oil recovery factor using machine learning: Applications of XGBoost classification—0
FairCanary: Rapid Continuous Explainable Fairness—0
Estimate the building height at a 10-meter resolution based on Sentinel data—0
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates—0
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation—0
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
Feature Selection for Discovering Distributional Treatment Effect Modifiers—0
FAST: Feature Arrangement for Semantic Transmission—0
Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting—0
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning—0
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis—0
FeatNavigator: Automatic Feature Augmentation on Tabular Data—0
Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification—0
Error-controlled non-additive interaction discovery in machine learning models—0
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context—0
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions—0
Feature Importance: A Closer Look at Shapley Values and LOCO—0
Continual Deterioration Prediction for Hospitalized COVID-19 Patients—0
Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness—0
Error-based Knockoffs Inference for Controlled Feature Selection—0
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models—0
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability—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