SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 890 papers

TitleStatusHype
Improved Bitcoin Price Prediction based on COVID-19 data—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up datasetCode0
The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of FeaturesCode0
Semantic match: Debugging feature attribution methods in XAI for healthcare—0
Instance-based Explanations for Gradient Boosting Machine Predictions with AXIL WeightsCode0
Predicting traffic crash severity using hybrid of balanced bagging classification and light gradient boosting machineCode0
Predicting Survival of Tongue Cancer Patients by Machine Learning ModelsCode0
Towards Cooperative Flight Control Using Visual-Attention—0
BMX: Boosting Natural Language Generation Metrics with ExplainabilityCode0
Discrimination, calibration, and point estimate accuracy of GRU-D-Weibull architecture for real-time individualized endpoint prediction—0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Predicting Knowledge Gain for MOOC Video ConsumptionCode0
Which products activate a product? An explainable machine learning approach—0
LatentSwap3D: Semantic Edits on 3D Image GANs—0
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal ForecastingCode0
Structural Knowledge Distillation for Object Detection—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement—0
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach—0
Estimating oil recovery factor using machine learning: Applications of XGBoost classification—0
Which is the best model for my data?—0
Abstract Interpretation-Based Feature Importance for SVMs—0
Greedy Modality Selection via Approximate Submodular Maximization—0
Show:102550
← PrevPage 21 of 36Next →
All datasetsbostonBreastcancerDiabetesDigitsirisWine

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