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Feature Importance

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평가표

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보존된 평가 기록

12

코드

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

https://github.com/Mind23-2/MindCode-45

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

https://github.com/MindSpore-scientific/code-1/tree/main/Patient2Vec-A-Personalized-Interpretable

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

https://github.com/OpenXAIProject/SHAP-Tutorial

A Unified Approach to Interpreting Model Predictions

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/fat_deepffm

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/

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

https://github.com/Prayforhanluo/CTR_Algorithm

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

https://github.com/QunBB/DeepLearning/blob/main/Recommendation/RANK/fibinet.py

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

https://github.com/RUCAIBox/EulerNet/blob/main/Model/Baselines/FiBiNet.py

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

https://github.com/Tiago-Roxo/Faces-in-the-Wild

Is Gender "In-the-Wild" Inference Really a Solved Problem?

https://github.com/Tiago-Roxo/YinYang-Net

Is Gender "In-the-Wild" Inference Really a Solved Problem?

https://github.com/TooTouch/WhiteBox-Part2

A Unified Approach to Interpreting Model Predictions

https://github.com/Trustworthy-AI-Group/TransferAttack

Feature Importance-aware Transferable Adversarial Attacks

https://github.com/TrustworthyAI-kr/LFI-CAM

LFI-CAM: Learning Feature Importance for Better Visual Explanation

https://github.com/UlionTse/mlgb

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

https://github.com/YinzhenWan/recome_wan

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

https://github.com/ZOMIN28/FIA-pytorch

Feature Importance-aware Transferable Adversarial Attacks

https://github.com/aai-institute/pyDVL

A Multilinear Sampling Algorithm to Estimate Shapley Values

https://github.com/alililia/ascend_Fat-DeepFFM

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

https://github.com/amiratag/InterpretationFragility

Interpretation of Neural Networks is Fragile

https://github.com/anonctr/GDCN/blob/main/models/FiBiNet.py

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

https://github.com/arjuns07/puextratrees

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

https://github.com/arnabdey929/Online-HFT-Stock-Forecasting-using-Clustering-and-RBFNN

Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks

https://github.com/arolihas/interpretable_deepPSL

Learning to Faithfully Rationalize by Construction

https://github.com/asmerdov/DataCollectionSystem

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

https://github.com/bdwilliamson/spvim_supplementary

Efficient nonparametric statistical inference on population feature importance using Shapley values

https://github.com/bdwilliamson/vimp

Efficient nonparametric statistical inference on population feature importance using Shapley values

https://github.com/bdwilliamson/vimpy

Efficient nonparametric statistical inference on population feature importance using Shapley values

https://github.com/bgreenwell/fastshap

A Unified Approach to Interpreting Model Predictions

https://github.com/brianferrell787/Visualizing-attention-layer-in-text-classification-deep-learning-algorithim

Attention is not Explanation

https://github.com/brianferrell787/Visualizing-attention-layer-in-text-classification-deep-learning-algorithm

Attention is not Explanation

https://github.com/bruce-willis/FiBiNET

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

https://github.com/cloudbopper/anamod

Feature Importance Explanations for Temporal Black-Box Models

https://github.com/codectr/RefineCTR/blob/main/FRCTR/model_zoo/fibinet.py

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

https://github.com/cran/tree.interpreter

A Debiased MDI Feature Importance Measure for Random Forests

https://github.com/csinva/local-vae

Interpretable machine learning: definitions, methods, and applications

https://github.com/csinva/transformation-importance

Interpretable machine learning: definitions, methods, and applications

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