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Feature Importance

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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/patrick-knab/dseg-lime

공식 코드

Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models

https://github.com/persimune/mait

공식 코드

Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses

https://github.com/peterbhase/ExplanationSearch

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The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations

https://github.com/pgeertsema/axil_paper

공식 코드

Instance-based Explanations for Gradient Boosting Machine Predictions with AXIL Weights

https://github.com/pidahbus/exclusion-inclusion

공식 코드

Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs

https://github.com/pievos101/cpath

공식 코드

Explaining and visualizing black-box models through counterfactual paths

https://github.com/piotr310100/epic

공식 코드

EPIC: Explanation of Pretrained Image Classification Networks via Prototype

https://github.com/plantedml/glex

공식 코드

Unifying local and global model explanations by functional decomposition of low dimensional structures

https://github.com/plantedml/glex_paper

공식 코드

Unifying local and global model explanations by functional decomposition of low dimensional structures

https://github.com/prdwb/UserIntentPrediction

공식 코드

User Intent Prediction in Information-seeking Conversations

https://github.com/puetpaper/PUExtraTrees

공식 코드

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

https://github.com/pytorch/captum

공식 코드

Captum: A unified and generic model interpretability library for PyTorch

https://github.com/qiaw99/fitcf

공식 코드

FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation

https://github.com/rccohn/deepspparks

공식 코드

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution

https://github.com/rcrupi/deepgrb

공식 코드

Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes

https://github.com/rehmanzafar/dlime_experiments

공식 코드

DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems

https://github.com/rezacsedu/XAI_Cancer_Prediction

공식 코드

OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data

https://github.com/ricsinaruto/meg-group-decode

공식 코드

Group-level Brain Decoding with Deep Learning

https://github.com/ruleminer/fi-rules4xai

공식 코드

Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance

https://github.com/safr-ai-lab/xai-disparity

공식 코드

Feature Importance Disparities for Data Bias Investigations

https://github.com/sandialabs/veesa

공식 코드

An Explainable Pipeline for Machine Learning with Functional Data

https://github.com/sbobek/lux

공식 코드

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations

https://github.com/sepehr-kamahi/faith

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Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models

https://github.com/shichangzh/gstarx

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GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games

https://github.com/shifwang/paper-debiased-feature-importance

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A Debiased MDI Feature Importance Measure for Random Forests

https://github.com/shouryaverma/interpretable_ml_ecg

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Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data

https://github.com/sianalytics/roar

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On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective

https://github.com/simulamet-host/SHAP

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Explainability of Machine Learning Models under Missing Data

https://github.com/singlasahil14/CASO

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Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

https://github.com/slds-lmu/paper_2024_guide_fi

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A Guide to Feature Importance Methods for Scientific Inference

https://github.com/smashhadi/feature-selection-extraction

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Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

https://github.com/smerdov/CS_GO_Sensors_Data_Analysis

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AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

https://github.com/smerdov/eSports_Sensors_Dataset

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

https://github.com/smerdov/smart_chair_client

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eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

https://github.com/snakoneczny/kids-quasars

공식 코드

Catalog of quasars from the Kilo-Degree Survey Data Release 3

https://github.com/sophhan/imlsa_2024

공식 코드

Interpretable Machine Learning for Survival Analysis

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