#Explainable-ml
Showing 28 of 28 repositories tagged #explainable-ml, ranked by stars
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Evaluation and Tracking for LLM Experiments and AI Agents
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
A library for graph deep learning research
A collection of research papers and software related to explainability in graph machine learning.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Interpretability and explainability of data and machine learning models
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation
XAI - An eXplainability toolbox for machine learning
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
OmniXAI: A Library for eXplainable AI
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Examples of Data Science projects and Artificial Intelligence use-cases
H2O.ai Machine Learning Interpretability Resources
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Model Agnostic Counterfactual Explanations
Real-time explainable machine learning for business optimisation
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
A large-scale database of malicious software images