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Feature Importance

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평가표

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보존된 평가 기록

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코드

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

https://github.com/cvjena/cs_parts

Classification-Specific Parts for Improving Fine-Grained Visual Categorization

https://github.com/datawhalechina/fun-rec

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/deepffm.py

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/fibinet.py

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/dbash/zerowaste

RISE: Randomized Input Sampling for Explanation of Black-box Models

https://github.com/dcmocanu/sparse-evolutionary-artificial-neural-networks

Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

https://github.com/deezer/functional_attribution

Towards Rigorous Interpretations: a Formalisation of Feature Attribution

https://github.com/dylan-slack/modeling-uncertainty-local-explainability

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

https://github.com/edpclau/ROAR-for-Scikit

A Benchmark for Interpretability Methods in Deep Neural Networks

https://github.com/ehorel/SFIT

Computationally Efficient Feature Significance and Importance for Machine Learning Models

https://github.com/fintechstanford/SFIT

Computationally Efficient Feature Significance and Importance for Machine Learning Models

https://github.com/francesco-borsatti-unipd/exiffi

Enhancing Interpretability and Generalizability in Extended Isolation Forests

https://github.com/ftorres11/saliencysense

RISE: Randomized Input Sampling for Explanation of Black-box Models

https://github.com/g8a9/ferret

Discretized Integrated Gradients for Explaining Language Models

https://github.com/galdeia/iirsbenchmark

Understanding Global Feature Contributions With Additive Importance Measures

https://github.com/gcskoenig/fippy

Relative Feature Importance

https://github.com/gcskoenig/rfi

Relative Feature Importance

https://github.com/google-research-datasets/bam

Benchmarking Attribution Methods with Relative Feature Importance

https://github.com/grahamannett/tellem

Captum: A unified and generic model interpretability library for PyTorch

https://github.com/huangjunheng/recommendation_model

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/hysts/pytorch_D-RISE

RISE: Randomized Input Sampling for Explanation of Black-box Models

https://github.com/iFe1er/AlitaNet

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/iamollas/LionLearn/tree/master/LioNets_V2

LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

https://github.com/iancovert/shapley-regression

A Unified Approach to Interpreting Model Predictions

https://github.com/interpretml/DiCE

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

https://github.com/jessevig/bertviz

Attention is not Explanation

https://github.com/joakimedin/medical-coding-reproducibility

An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records

https://github.com/jpmorganchase/cf-shap-facct22

Counterfactual Shapley Additive Explanations

https://github.com/koalaverse/vip

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

https://github.com/laura-rieger/deep-explanation-penalization

Interpretable machine learning: definitions, methods, and applications

https://github.com/linkedin/fasttreeshap

A Unified Approach to Interpreting Model Predictions

https://github.com/liuyanguu/SHAPforxgboost

A Unified Approach to Interpreting Model Predictions

https://github.com/luxinming/PaddleRec20220725/tree/master/models/rank/fat_deepffm

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/lyhue1991/eat_pytorch_in_20_days

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/matteo-rizzo/explainable-fruit-ripeness-classification

A Unified Approach to Interpreting Model Predictions

https://github.com/mcvidomi/MFI

Feature Importance Measure for Non-linear Learning Algorithms

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.