Awesome GitHub RepositoriesFeature Interaction Visualizations

SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to

This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro

Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu

هذا المشروع عبارة عن مورد تعليمي شامل ودليل تقني يركز على تعلم الآلة القابل للتفسير والذكاء الاصطناعي القابل للشرح. يعمل ككتاب مدرسي ومرجع لتنفيذ التقنيات التي تجعل نماذج تعلم الآلة المعقدة شفافة ومفهومة للبشر. يوفر المورد إرشادات حول بناء نماذج شفافة بطبيعتها، مثل أشجار القرار والنماذج الخطية المتفرقة، وتطبيق طرق الشرح اللاحقة على أنظمة الصندوق الأسود. يفصل المنهجيات المحددة لقياس أهمية الميزة، وتوليد مبررات للتنبؤات الفردية، واستخدام نماذج بديلة لتقريب عمليات صنع القرار المعقدة. يغطي المحتوى مجموعة واسعة من القدرات التحليلية، بما في ذلك تحليل تأثير الميزة العالمية والمحلية، وقابلية تفسير رؤية الكمبيوتر، واستخدام المساهمات القائمة على نظرية الألعاب مثل قيم Shapley. كما يتناول تقييم النموذج من خلال تقييمات القابلية للتفسير، وسير عمل تصحيح الأخطاء لتحديد اختصارات النموذج، وتصميم هياكل الخوارزميات الشفافة. يتم تنفيذ المشروع كمجموعة من دفاتر Jupyter.

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable