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A collection of 3 posts

Part II - Explainable ML Models: Local post hoc explanations

Part II - Explainable ML Models: Local post hoc explanations

Post hoc explanations approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Several local explanation methods are model-agnostic, meaning they do not have access to the internal structure of the model.

Explainable ML Models: what are explanations and why do we need them? – Part I

Explainable ML Models: what are explanations and why do we need them? – Part I

Interpretability is a key element of trust for AI models. An explanation is an interpretable description of a model behavior. For an explanation to be valid it needs to be faithful to the model and it needs to be understandable to the user.