SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 626–650 of 890 papers

TitleStatusHype
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification—0
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics—0
A Comparative Study on Machine Learning-based Approaches for Improving Traffic Accident Severity PredictionCode0
Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records—0
A Survey on the Robustness of Feature Importance and Counterfactual Explanations—0
On the explainability of hospitalization prediction on a large COVID-19 patient dataset—0
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon SetCode0
Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasoundsCode0
ML-Based Analysis to Identify Speech Features Relevant in Predicting Alzheimer's Disease—0
Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolutionCode0
On Predictive Explanation of Data Anomalies—0
Lexicon Creation for Interpretable NLP Models—0
Clustering-Based Interpretation of Deep ReLU NetworkCode0
Logic Constraints to Feature Importances—0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
On the Trustworthiness of Tree Ensemble Explainability Methods—0
Interpreting Black-boxes Using Primitive Parameterized Functions—0
Mask and Understand: Evaluating the Importance of Parameters—0
Cluster-based Feature Importance Learning for Electronic Health Record Time-series—0
Towards trustworthy explanations with gradient-based attribution methods—0
Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasetsCode0
Ranking Feature-Block Importance in Artificial Multiblock Neural NetworksCode0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.76—Unverified
2Garson Variable ImportancePearson 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