Cuestionario vinculado a una guía · Medio Nivel

Prueba SVM de una clase

Revise el límite de novedad de One-Class SVM, las funciones de nu y gamma, y los límites de las puntuaciones aprendidas a partir de datos de referencia.

Rutas de guía relacionadasSvm de una clase
Pregunta 1 de 8

What data does One-Class SVM commonly use to learn a novelty boundary?

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  1. What data does One-Class SVM commonly use to learn a novelty boundary?
    • A reference sample intended to represent normal behavior
    • Balanced labels for every anomaly and inlier
    • Only the future query points
    • A sequence of cluster IDs from k-means
  2. What does a later point outside the learned boundary mean?
    • It is definitively a failure
    • It is novel under this fitted representation and warrants interpretation
    • It has a calibrated probability of being fraudulent
    • It must have been excluded from the reference data by error
  3. For an RBF kernel, what does gamma control?
    • The exact fraction of future cases that are wrong
    • The number of anomaly classes
    • How quickly similarity declines with distance
    • The response variable's unit
  4. How should nu be interpreted in the common formulation?
    • A lower bound on training errors and an upper bound on support vectors
    • An exact estimate of population anomaly prevalence
    • A probability that a future point is anomalous
    • An upper bound on training errors and lower bound on support vectors
  5. Why scale features before fitting a kernel SVM?
    • Scaling sets the correct nu automatically
    • Feature units affect distances and therefore the kernel boundary
    • The kernel ignores all feature values after scaling
    • It creates labels for unknown anomalies
  6. What can happen if reference training data contain many anomalous points?
    • The model becomes supervised by itself
    • Nu guarantees all anomalies are removed
    • The learned boundary may treat some anomalies as normal
    • The kernel changes to a Gaussian response family
  7. Why can benign process drift create false novelty alerts?
    • All future points are used to refit the boundary
    • Gamma automatically detects drift labels
    • Nu is the exact real anomaly rate
    • A fixed boundary may no longer cover changed normal behavior
  8. Which statement about the decision score is appropriate?
    • It reflects position relative to a fitted boundary, not a causal diagnosis
    • It is a calibrated probability by definition
    • It directly identifies why the point is unusual
    • It remains unchanged under every scaling choice