Cuestionario vinculado a una guía · Medio Nivel

Prueba de análisis discriminante lineal

Revise la clasificación y proyección de LDA, los supuestos de covarianza compartida, las dimensiones discriminantes y la distinción de PCA.

Rutas de guía relacionadasAnálisis discriminante lineal
Pregunta 1 de 8

Why does common LDA classification produce linear decision boundaries?

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  1. Why does common LDA classification produce linear decision boundaries?
    • It uses class-specific covariance matrices in every case
    • It maximizes total feature variance without labels
    • Classes share a covariance matrix in its usual Gaussian model
    • It ignores class priors whenever class sizes differ
  2. How does LDA choose projection directions in its supervised reduction role?
    • Maximize total variance without labels
    • Favor class separation relative to within-class variation
    • Choose features only by their marginal variance
    • Keep only the original columns with largest means
  3. With K classes and at least K minus one input features, what upper bound applies to the number of useful LDA discriminant directions?
    • K squared
    • K
    • The number of observations
    • K minus one
  4. Which objective does PCA optimize in contrast to LDA?
    • High overall variance without using class labels
    • Class posterior probability
    • Maximum class separation using labels
    • Minimum classification error at every threshold
  5. When may a low-variance direction matter to LDA?
    • When it has the smallest variance regardless of labels
    • Only when PCA has already chosen it
    • When it separates labeled classes
    • Only when every class has the same mean on it
  6. What changes in quadratic discriminant analysis compared with common LDA?
    • Each class must have the same prior probability
    • Each class may have its own covariance matrix
    • Every class mean is constrained to be equal
    • All quadratic terms are removed from the class comparison
  7. Why can LDA be numerically difficult when features greatly outnumber examples?
    • The number of classes must automatically become larger
    • Class priors cannot be estimated from any labeled sample
    • The shared-covariance assumption removes every conditioning problem
    • Covariance estimates may be singular or unstable
  8. A plot shows separated training groups after LDA projection. What has not yet been established?
    • Reliable generalization on held-out data
    • That labels influenced the projection
    • That a projection was computed
    • That classes were present in training