PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random ...

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.

GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects.

“Rather than trying to create models that are inherently interpretable, there has been a recent explosion of work on ‘explainable ML’, where a second (post hoc) model is created to explain ...

Consider the Type of Machine Learning Model. The type of machine learning model you are using also plays a crucial role in the selection of explainable AI tools. Some tools are designed for specific ...

Tree-based models have been successfully applied to a wide variety of tasks, including time series forecasting. They are increasingly in demand and widely accepted because of their comparatively high ...

Explainable AI: 5 Desired Frameworks in Python. InterpretML: Simplifies complex models with model-agnostic interpretability tools SHAP (Shapley Additive exPlanations): Provides unified and coherent ...