#Shap
Showing 21 of 21 repositories tagged #shap, ranked by stars
A game theoretic approach to explain the output of any machine learning model.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Shapley Interactions and Shapley Values for Machine Learning
Fast SHAP value computation for interpreting tree-based models
A power-full Shapley feature selection method.
Automated Tool for Optimized Modelling
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.
SHAP Plots in R
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
Real-time explainable machine learning for business optimisation
How to Interpret SHAP Analyses: A Non-Technical Guide
Local explanations with uncertainty 💐!
The AI-Powered Healthcare Intelligence Network is an AI-driven system offering disease prediction, drug recommendations, heart disease risk assessment, and an AI medical chatbot. Using ML, NLP, and LLMs, it provides accurate diagnoses, insights, and recommendations, enhancing healthcare accessibility, efficiency, and decision-making .
A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.
iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python
This project proposes a novel methodology to automatically learn financial lexicons that outperform the benchmark Loughran-McDonald lexicon in sentiment analysis tasks
This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.
f1 race winner predictor