quiz bu lëkkale ak tegtal · Diggante Niveau

IA ak SHAP Quiz buñu mëna leeral

Xoolaat ni SHAP di joxee ay génne model, li plot local ak summary di wane, ak lu tax leeral yi soxla tekki bu jaar yoon.

Yooni njiit yu jëm ciIA buñu mëna leeral ak jëmm
Laaj 1 de 8

Ban konsept math lañu sukkandikoo ci valeur SHAP yi?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Ban konsept math lañu sukkandikoo ci valeur SHAP yi?
    • Valeur bu Shapley ci theorie jeu kooperatif
    • bayesien yi ci ginaaw
    • Wàcci degrade
    • Càmbaru lëf li gëna am solo
  2. 'Additivity' (njubte ci barab bi) màndarga SHAP dafay tekki ni ci benn waxtaan, SHAP dafay am valeur...
    • Ndax ñépp dañu baax
    • boole ci wuute gi nekk ci digganté liñu séentu ak liñuy génne
    • Dañu leen normalise ñu nekk ci diggante 0 ak 1
    • Dañuy méngale njubteg tàggat model bi
  3. Lan moo waral xayma valeur Shapley yi seer lool?
    • Dafay laaj ñu tàggataat model bi yoon yu bari
    • Dafa soxla ay done yuñ etikete ngir bepp wax luy waaja am
    • Dafa wara xayma li benn man-man mëna def ci bépp ensemble bu ndaw bu mëna am ci man-man yi
    • Ci GPU yi kese lay dox
  4. Ban xeetu SHAP mooy xayma valeur yi gëna jubal ci xeetu garab yu am gradient?
    • Formu noyau
    • DeepSHAP
    • forme ligneaire
    • Formu garab
  5. Why do teams use explanation methods such as SHAP during model development?
    • To inspect model behavior, investigate influential features, and support review
    • Because SHAP proves the model is fair
    • Because every law requires SHAP for every model
    • Because feature attributions establish causal effects
  6. What can a SHAP summary plot show across a dataset?
    • Whether a feature caused the outcome in the real world
    • How much features affect model outputs across examples, including the spread of their SHAP values
    • Which individual training row leaked the target label
    • A guaranteed ranking of features for every possible dataset
  7. What does a local SHAP explanation describe?
    • A causal effect of changing a real-world feature
    • A guarantee that the model is fair
    • Feature attributions for one prediction relative to a chosen baseline and feature convention
    • The global training accuracy for every dataset
  8. Why can correlated features complicate SHAP interpretation?
    • Correlated features make all SHAP values exactly zero
    • Correlation proves one feature caused the prediction
    • SHAP does not use a model output
    • Different assumptions about feature dependence can change how contributions are allocated