Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ṣe alaye AI ati adanwo SHAP

Review how SHAP attributes model outputs, what local and summary plots show, and why explanations need careful interpretation.

Jẹmọ itọsọna awọn ọnaṢe alaye AI Ati Shap
Ibeere 1 ti 8

Agbekale mathematiki wo ni awọn iye SHAP da lori?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Agbekale mathematiki wo ni awọn iye SHAP da lori?
    • Awọn iye Shapley lati ajumose game ero
    • Bayesian ẹhin
    • Isosile ite
    • Itupalẹ paati akọkọ
  2. Ohun-ini 'afikun' (ipeye agbegbe) ti SHAP tumọ si pe fun asọtẹlẹ kan, awọn iye SHAP…
    • Gbogbo wa ni rere
    • Apapọ si iyatọ laarin asọtẹlẹ ati iṣelọpọ ipilẹ kan
    • Ti ṣe deede lati dubulẹ laarin 0 ati 1
    • Dogba deede ikẹkọ awoṣe
  3. Kini idi ti iṣiro gangan awọn iye Shapley jẹ gbowolori ni gbogbogbo?
    • O nilo atunṣe awoṣe ni ọpọlọpọ igba
    • O nilo data idanwo aami fun gbogbo asọtẹlẹ
    • O gbọdọ ṣe aropin idasi alapin ẹya kan lori gbogbo awọn ipin ti o ṣeeṣe ti awọn ẹya
    • O ṣiṣẹ nikan lori awọn GPU
  4. Iyatọ SHAP wo ni o ṣe iṣiro awọn iye deede daradara fun awọn awoṣe igi ti o ni igbega gradient?
    • KernelSHAP
    • DeepSHAP
    • LinearSHAP
    • Igi SHAP
  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