SOTAVerified

Feature Importance

Papers

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Showing 326–350 of 890 papers

TitleStatusHype
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
Handling Missing Values in Local Post-hoc ExplainabilityCode0
On Feature Importance and Interpretability of Speaker Representations—0
Model-agnostic variable importance for predictive uncertainty: an entropy-based approachCode0
Automatic prediction of mortality in patients with mental illness using electronic health records—0
DANAA: Towards transferable attacks with double adversarial neuron attributionCode0
Characterizing climate pathways using feature importance on echo state networks—0
Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data—0
Enhancing Interpretability and Generalizability in Extended Isolation ForestsCode0
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class—0
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates—0
Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks—0
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
Modality-aware Transformer for Financial Time series Forecasting—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Axiomatic Aggregations of Abductive Explanations—0
Tell Me a Story! Narrative-Driven XAI with Large Language ModelsCode0
Explainable machine learning-based prediction model for diabetic nephropathy—0
Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries—0
Physics Inspired Hybrid Attention for SAR Target RecognitionCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
Visualizing Topological Importance: A Class-Driven Approach—0
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation—0
A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection—0
Can local explanation techniques explain linear additive models?Code0
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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