shap
There are 708 repositories under shap topic.
shap/shap
A game theoretic approach to explain the output of any machine learning model.
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
oegedijk/explainerdashboard
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
cerlymarco/shap-hypetune
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
linkedin/FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
jiangnanboy/learning_to_rank
利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.
predict-idlab/powershap
A power-full Shapley feature selection method.
feedzai/timeshap
TimeSHAP explains Recurrent Neural Network predictions.
tvdboom/ATOM
Automated Tool for Optimized Modelling
ing-bank/probatus
SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.
AstraZeneca/awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
ModelOriented/shapviz
SHAP Plots in R
MI2DataLab/survshap
SurvSHAP(t): Time-dependent explanations of machine learning survival models
ModelOriented/treeshap
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
nredell/ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
snehankekre/streamlit-shap
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
dylan-slack/Fooling-LIME-SHAP
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
nredell/shapFlex
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
xplainable/xplainable
Real-time explainable machine learning for business optimisation
AidanCooper/shap-clustering
How to use SHAP values for better cluster analysis
AidanCooper/shap-analysis-guide
How to Interpret SHAP Analyses: A Non-Technical Guide
ModelOriented/kernelshap
Different SHAP algorithms
TannerGilbert/Model-Interpretation
Overview of different model interpretability libraries.
dylan-slack/Modeling-Uncertainty-Local-Explainability
Local explanations with uncertainty 💐!
GeoAIR-lab/XAI-tool4GEE
A Colab notebook for land cover mapping and monitoring using Earth Engine
mayer79/LightSHAP
Lightweight Python implementation of SHAP
marvinbuss/ExplainableML-Vision
This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.
cloudera/CML_AMP_Explainability_LIME_SHAP
Learn how to explain ML models using LIME and SHAP.
ata-turhan/Titanic-Survival-Prediction
A comprehensive solution for the Kaggle Titanic Challenge, featuring advanced data exploration, feature engineering, model training, and explainable AI techniques. Includes Logistic Regression, RandomForest, XGBoost, and Stacked Ensembles with SHAP and permutation importance for model interpretability.
EnbinYang/tb_prediction_files
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
kahramankostas/IoTGeM
IoT Attack Detection with machine learning
EricKenjiLee/WaveMAP_Paper
This repo allows for the complete reproduction, from processed data, of all the main and supplemental figures in the manuscript Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex.
PrashantSaikia/Dynamic-SHAP-Plots
Enabling interactive plotting of the visualizations from the SHAP project.
AliAmini93/Telecom-Churn-Analysis
Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.