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.

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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 ...

“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 ...

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

Explainable AI is being applied across various industries, providing transparency in critical AI systems. Healthcare: XAI tools help clinicians understand the predictions of AI models used in ...