Guide-yakabatana mibvunzo · Pakati Nhanho

Inotsanangurwa AI uye SHAP Quiz

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

Nzira dzekutungamira dzinoenderanaInotsanangurwa AI Uye Shap
Mubvunzo 1 ye 8

Ndeipi pfungwa yemasvomhu yakavakirwa paSHAP tsika?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeipi pfungwa yemasvomhu yakavakirwa paSHAP tsika?
    • Iko kukosha kweShapley kubva kune cooperative game theory
    • Bayesian posteriors
    • Gradient descent
    • Principal component analysis
  2. Iyo 'additivity' (yenzvimbo chaiyo) pfuma yeSHAP inoreva kuti kune imwe fungidziro, iyo SHAP inokoshesa...
    • Vese vari positive
    • Pfupiso pamusiyano uripo pakati pekufanotaura uye yekutanga kubuda
    • Idzo dzakajairwa kureva nhema pakati pe0 ne1
    • Enzanisa kurongeka kwekudzidziswa kwemuenzaniso
  3. Nei komputa chaiyo Shapley tsika kuchiwanzo kudhura?
    • Zvinoda kudzokorora modhi yacho kakawanda
    • Inoda yakanyorwa test data yefembero yese
    • Inofanirwa kuenzana nechikamu chemarigineti yekubatsira pane zvese zvinogoneka zvikamu zvezvimiro
    • Inoshanda chete paGPUs
  4. Ndeipi SHAP mutsauko inokokorodza chaiyo kukosha kwemhando yemiti yakawedzera gradient?
    • 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