Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska AI iyo SHAP la macnayn karo

Dib u eeg sida ay SHAP u tilmaanto wax-soo-saarka moodeelka, waxa ay muujinayaan muuqaallada maxalliga ah iyo kuwa kooban, iyo sababta ay sharraxaadyadu ugu baahan yihiin fasiraad taxaddar leh.

Wadooyinka haga ee la xidhiidhaLa Sharaci karo AI iyo Shap
Su'aal 1 ee 8

Waa maxay fikradda xisaabeed ee ay ku salaysan yihiin qiyamka SHAP?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay fikradda xisaabeed ee ay ku salaysan yihiin qiyamka SHAP?
    • Qiimaha Shapley ee aragtida ciyaarta iskaashiga
    • Gadaasha Bayesian
    • Soo degid tartiib ah
    • Falanqaynta qaybta ugu muhiimsan
  2. Hantida 'wax-ku-darka' (saxnimada deegaanka) ee hantida SHAP waxay la macno tahay in hal saadaal, SHAP ay qiimayso...
    • Dhammaantood waa kuwo togan
    • Isku soo wada duuboo farqiga u dhexeeya saadaasha iyo wax soo saarka gundhigga
    • Caadiyan inay u dhexeyso 0 iyo 1
    • Isku mid saxnaanta tababarka moodeelka
  3. Waa maxay sababta xisaabinta saxda ah ee qiyamka Shapley ay guud ahaan qaali u tahay?
    • Waxay u baahan tahay in dib loo tababaro qaabka marar badan
    • Waxay u baahan tahay xog tijaabo ah oo calaamadeysan saadaal kasta
    • Waa in ay celcelis ahaan wax ku biirinta sifadu ka sarraysaa dhammaan qaybaha suurtogalka ah ee sifooyinka
    • Waxa kaliya oo ay ka shaqeysaa GPU-yada
  4. Kala duwanaanshiyaha SHAP kee u xisaabiya qiyamka saxda ah si wax ku ool ah oo loogu talagalay moodooyinka geedka gradient-ka?
    • KernelSHAP
    • DeepSHAP
    • LinearSHAP
    • TreeSHAP
  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