Prueba de métodos de selección de funciones

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿En qué se diferencia la selección de características del análisis de componentes principales?
   • Selection and PCA both construct the same linear projections
   • Selection chooses data rows while PCA chooses original columns
   • Selection retains input variables; PCA constructs transformed components
   • Selection is necessarily unsupervised while PCA requires class labels
2. Un filtro univariado puede omitir una variable que es útil sólo ¿a través de qué?
   • Its variance considered in isolation
   • An interaction with another feature
   • The column's frequency of missing values alone
   • Its linear correlation with the target alone
3. ¿Qué hace que la eliminación recursiva de funciones sea un método contenedor?
   • It computes only a label-free variance threshold
   • It transforms every feature into a principal component
   • It fixes an initial feature ranking without refitting
   • It repeatedly fits an estimator to judge feature subsets
4. Un modelo lineal regularizado L1 establece algunos coeficientes en cero durante el ajuste. ¿Este es un ejemplo de qué familia?
   • Embedded selection
   • An estimator-independent univariate filter
   • An external search that refits every candidate subset
   • An unsupervised component projection
5. ¿Por qué se debe repetir la selección de funciones supervisadas dentro de cada pliegue de entrenamiento?
   • To guarantee identical selected variables in every fold
   • To avoid tuning the selected subset size
   • To prevent held-out labels from affecting the selected subset
   • To make training accuracy an unbiased test score
6. How should high mutual information with a target be interpreted?
   • Evidence of statistical dependence, not proof of causation
   • Proof the feature will help every estimator
   • Proof the feature is independent of others
   • A guarantee of stable performance in production
7. Two correlated variables split a tree model's importance. What should an analyst conclude?
   • Both variables are proven useless
   • The lower score proves negative causation
   • Importance scores are unaffected by correlation
   • Importance can be distributed among substitutes, so inspect redundancy
8. A selected subset changes greatly across resamples. Which diagnostic is most directly relevant?
   • Selection stability across resamples
   • The training score from a single fitted subset
   • The marginal variance of one selected column
   • The number of input variables before selection