SOTAVerified

Feature Importance

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–890 of 890 papers

TitleStatusHype
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery—0
Data Strategies for Fleetwide Predictive Maintenance—0
A matching based clustering algorithm for categorical data—0
Catalog of quasars from the Kilo-Degree Survey Data Release 3Code0
Learning Interpretable Characteristic Kernels via Decision Forests—0
Differential Private Stack Generalization with an Application to Diabetes Prediction—0
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health RecordCode0
A Genre-Aware Attention Model to Improve the Likability Prediction of BooksCode0
Interpreting Neural Networks With Nearest NeighborsCode0
Rule induction for global explanation of trained modelsCode0
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured DataCode0
Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks—0
PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques—0
Knockoffs for the mass: new feature importance statistics with false discovery guarantees—0
A Benchmark for Interpretability Methods in Deep Neural NetworksCode0
Visualizing the Feature Importance for Black Box ModelsCode0
Predicting Good Configurations for GitHub and Stack Overflow Topic Models—0
A Dynamic-Adversarial Mining Approach to the Security of Machine Learning—0
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping—0
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural NetworksCode0
Building effective deep neural networks one feature at a time—0
INTERPRETATION OF NEURAL NETWORK IS FRAGILE—0
Variable Importance Using Decision Trees—0
AutoLearn - Automated Feature Generation and SelectionCode0
Feature importance scores and lossless feature pruning using Banzhaf power indices—0
Interpretation of Neural Networks is FragileCode0
Random Forest DBSCAN for USPTO Inventor Name Disambiguation—0
A Broad Learning Approach for Context-Aware Mobile Application Recommendation—0
A hybrid supervised/unsupervised machine learning approach to solar flare prediction—0
Consistent feature attribution for tree ensemblesCode0
Building effective deep neural network architectures one feature at a time—0
High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in ^123I-Ioflupane SPECT Imaging—0
Feature Importance Measure for Non-linear Learning AlgorithmsCode0
Gradients of Counterfactuals—0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
Text Analysis and Automatic Triage of Posts in a Mental Health Forum—0
Discrete Deep Feature Extraction: A Theory and New Architectures—0
Likely to stop? Predicting Stopout in Massive Open Online Courses—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—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