shap
There are 351 repositories under shap topic.
shap/shap
A game theoretic approach to explain the output of any machine learning model.
mljar/mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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.
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
AstraZeneca/awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
ing-bank/probatus
Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
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.
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
dylan-slack/Fooling-LIME-SHAP
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
ModelOriented/shapviz
SHAP Plots in R
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
TannerGilbert/Model-Interpretation
Overview of different model interpretability libraries.
AidanCooper/shap-analysis-guide
How to Interpret SHAP Analyses: A Non-Technical Guide
dylan-slack/Modeling-Uncertainty-Local-Explainability
Local explanations with uncertainty 💐!
ModelOriented/kernelshap
Different SHAP algorithms
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.
GeoAIR-lab/XAI-tool4GEE
A Colab notebook for land cover mapping and monitoring using Earth Engine
EnbinYang/tb_prediction_files
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
PrashantSaikia/Dynamic-SHAP-Plots
Enabling interactive plotting of the visualizations from the SHAP project.
ds-wook/ai-hackathon
🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
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.
Raman-Lab-UCLA/Explainability_for_Photonics
Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067
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.
VishalKumar-S/Sales_Conversion_Optimization_MLOps_Project
Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.
JK-Future-GitHub/NBA_Champion
I will predict the 2023 NBA Champion using Machine Learning