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Adversarial Mienzaniso uye Kusimba
Tekinoroji
Nhungamiro yehunyanzvi
Inotsanangurwa AI uye SHAP
SHAP assigns feature contributions to a model prediction using Shapley-value ideas.
Teerera kugwaro iriAI-yakarondedzerwa · ~1 min
Pa peji ino1 min verenga
Pfupiso
An explanation describes model behavior under a chosen background and feature-dependence setup; it is not automatically causal or legally sufficient.
Kudzika Kwakadzika
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.
Kuitwa muNyika yeChokwadi
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.
Ramangwana reInotsanangurwa AI uye SHAP
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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Mibvunzo inowanzo bvunzwa
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.
Ndeipi pfungwa yemasvomhu yakavakirwa paSHAP tsika?
SHAP inogadzirisa kukosha kweShapley, iyo inogovera zvakaringana 'kubhadhara' (kufanotaura) pakati pe'vatambi' (zvimiro zvekuisa) zvichibva pane zvavanoita.
Nei komputa chaiyo Shapley tsika kuchiwanzo kudhura?
Chaiyo Shapley tsika dzinofunga zvese zvinogoneka kurongeka / subset yezvimiro, izvo zvinokura zvakanyanya nenhamba yezvimiro.
Ndeipi SHAP mutsauko inokokorodza chaiyo kukosha kwemhando yemiti yakawedzera gradient?
TreeSHAP inoshandisa chimiro chemiti yesarudzo kuverengera chaiwo Shapley kukosha mupolynomial kwete nguva yeexponential.
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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