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Feature Importance

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/bips-hb/carfi_paper

공식 코드

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

https://github.com/bips-hb/cfi_mixeddata

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Conditional Feature Importance for Mixed Data

https://github.com/bityaneuhof/confident_feature_ranking

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Confident Feature Ranking

https://github.com/blue-yonder/tsfresh

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Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/braindatalab/scrutinizing-xai

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Scrutinizing XAI using linear ground-truth data with suppressor variables

https://github.com/braindatalab/xai-tris

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XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance

https://github.com/braisCB/E2E-FS

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E2E-FS: An End-to-End Feature Selection Method for Neural Networks

https://github.com/cae67/G2PC_L2PC

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Algorithm-Agnostic Explainability for Unsupervised Clustering

https://github.com/caglarkucuk/earthformer-satellite-to-radar

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Transformer-based nowcasting of radar composites from satellite images for severe weather

https://github.com/ccnu-mathits/adm

공식 코드

Automated discovery of symbolic laws governing skill acquisition from naturally occurring data

https://github.com/celiotrois/posidonia_biogeochemical

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Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water

https://github.com/chiarabales/unsupervised_sv

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Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

https://github.com/christelsirocchi/rule-graph

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Enhancing interpretability of rule-based classifiers through feature graphs

https://github.com/christophM/paper_conditional_subgroups

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Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach

https://github.com/cimagroup/application_representative_measure_reliability_dt

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Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data

https://github.com/clips/interpret_with_rules

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Rule induction for global explanation of trained models

https://github.com/compstat-lmu/code_pitfalls_iml

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General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

https://github.com/covit2019/analysis_codes

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A copula-based visualization technique for a neural network

https://github.com/cripac-dig/destine

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Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

https://github.com/crownpku/federated_shap

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Interpret Federated Learning with Shapley Values

https://github.com/csinva/disentangled-attribution-curves

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Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

https://github.com/csinva/hierarchical-dnn-interpretations

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Hierarchical interpretations for neural network predictions

https://github.com/csinva/imodels

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Interpretable machine learning: definitions, methods, and applications

https://github.com/cwayad/shapleychains

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Shapley Chains: Extending Shapley Values to Classifier Chains

https://github.com/d909b/ame

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Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

https://github.com/d909b/cxplain

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CXPlain: Causal Explanations for Model Interpretation under Uncertainty

https://github.com/data-sciencehub/gpce-sensitivity

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Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models

https://github.com/dataresponsibly/sharp

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ShaRP: A Novel Feature Importance Framework for Ranking

https://github.com/ddofer/cah

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Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game

https://github.com/deepinmotion/shapgcn

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Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition

https://github.com/deepu099cse/multi-labeled-bengali-toxic-comments-classification

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Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning

https://github.com/delyan-boychev/pytorch_trainers_interpretability

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Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection

https://github.com/dfki-nlp/smv

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Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods

https://github.com/dgrdn08/raus

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Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury Before Onset

https://github.com/dl-wg/gcn-lstm-wildfire

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Explainable Global Wildfire Prediction Models using Graph Neural Networks

https://github.com/dmirlab-group/fans

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Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation

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