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local explanations

A collection of 2 posts
Part II - Explainable ML Models: Local post hoc explanations
xai

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.
May 25, 2021 3 min read
Explainable ML Models: what are explanations and why do we need them? – Part I
xai

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.
May 22, 2021 3 min read
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