GUIDE teknik
IA ak SHAP yuñ mëna leeral
SHAP assigns feature contributions to a model prediction using Shapley-value ideas.
Déglul tegtal biiIA-nettali · ~1 min
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Résumé
An explanation describes model behavior under a chosen background and feature-dependence setup; it is not automatically causal or legally sufficient.
Plongeur bu xóot
SHAP methods use Shapley-value concepts to allocate a prediction among features. TreeSHAP provides efficient exact calculations for supported tree ensembles. Explanations depend on the model, reference distribution, feature-dependence assumptions, and output scale. A contribution indicates how the model output changes under that method; it does not establish that a feature caused the outcome, that the model is fair, or that an explanation satisfies a legal duty.
Doxal ci àdduna dëgg
Inspect feature contributions for a particular prediction.
Compare contribution distributions across a dataset.
Check how correlated features affect attribution.
Document model, background data, and explanation settings.
Ëlëgu IA ak SHAP yuñ mëna leeral
Interpretability methods can support debugging and auditing when assumptions are disclosed. Combine them with performance, subgroup, causal, and domain analyses; do not present SHAP as a standalone fairness or compliance test.
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Take the Explainable AI and SHAP quiz
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What is Explainable AI and SHAP?
SHAP assigns feature contributions to a model prediction using Shapley-value ideas. An explanation describes model behavior under a chosen background and feature-dependence setup; it is not automatically causal or legally sufficient.
Ban konsept math lañu sukkandikoo ci valeur SHAP yi?
SHAP dafay méngale valeur Shapley, biy séddale ci anam wu jaar yoon 'payoment' (li ñuy wax luy waaja am) ci 'joueur yi' (man-mani dugal yi) sukkandikoo ci seen contribution.
Lan moo waral xayma valeur Shapley yi seer lool?
Valeur yu Shapley yi dañuy xoolaat bépp xeetu man-man/subset bu mëna am, buy màgg exponentiellement ak limu man-man yi.
Ban xeetu SHAP mooy xayma valeur yi gëna jubal ci xeetu garab yu am gradient?
TreeSHAP dafay jëfandikoo jumtukaayu garabi dogal ngir xayma valeur yu Shapley ci polynomial te du exponentiel.
Why do teams use explanation methods such as SHAP during model development?
SHAP can help inspect a model’s predictions, but legal duties vary and attribution does not establish causality or fairness.
What can a SHAP summary plot show across a dataset?
SHAP documentation describes summary plots as showing feature impact across instances; plotted SHAP values display the range and distribution of those effects.
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