Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Luminta Hinge

Xisaabi luminta hinge ee xad-dhaafka saxeexan oo ku xidh xad-gudubyada margin ujeedooyinka SVM iyo kala soocida luminta saadka.

Wadooyinka haga ee la xidhiidhaLuminta Hinge
Su'aal 1 ee 8

A correctly labeled example has signed margin 0.4. What is its hinge loss?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. A correctly labeled example has signed margin 0.4. What is its hinge loss?
    • 0.6
    • 0
    • 1.4
    • 0.4
  2. Xagee la saxeexan yahay ayaa luminta hinge marka hore ku noqotaa eber?
    • Exactly zero only
    • One or greater
    • Any negative value
    • At minus one or less
  3. Kiis lagu calaamadiyay taban wuxuu helayaa buundada 0.5. Waa maxay xadkeeda saxeexan?
    • 1.5
    • 0.5
    • -0.5
    • -1.5
  4. Maxay tahay sababta dhibic si sax ah loo kala saaray u yeelan kartaa luminta hingeska?
    • The point must be mislabeled
    • Correct classification always means a negative score
    • Hinge loss ignores all margins
    • It can be on the correct side but inside the unit margin
  5. Sidee ayuu khasaaraha saadka ugu kala duwan yahay bar si sax ah loo kala saaray oo ka fog xadka?
    • It is always exactly zero
    • It continues to have a positive loss
    • It becomes a class probability itself
    • It is not defined for positive scores
  6. Ganacsigee ujeedo ganacsi ayaa ku badan SVM toos ah?
    • A clustering compactness term plus a tree-depth penalty
    • A squared-error term on numeric class labels plus a seasonal term
    • A weight-norm penalty balanced against hinge-loss violations
    • A probability calibration penalty balanced against class frequency
  7. Why should a decision score not be reported directly as a calibrated probability?
    • A decision score is already the observed event frequency
    • Any score above zero is automatically a calibrated probability
    • Every SVM estimates a generative distribution over classes
    • Hinge loss does not itself calibrate decision scores as probabilities
  8. What should be checked when comparing two SVM implementations called linear classifiers?
    • Whether the estimators use hinge or squared hinge and compatible regularization settings
    • Whether identical training accuracy implies that their loss definitions are equivalent
    • Whether raw loss values must match even when their loss definitions differ
    • Whether each implementation reaches 100% training accuracy