Prueba de codificación de objetivos

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

1. Una categoría ocurre en una fila de entrenamiento. ¿Por qué es peligroso utilizar el objetivo no suavizado de esa fila como su propia entrada?
   • The encoding necessarily becomes identical to the global mean
   • The category frequency alone reveals the model's validation score
   • The encoding directly reveals that row's outcome
   • An unsmoothed single-row encoding is protected from leakage by its small sample size
2. ¿Cómo debería recibir codificaciones de destino un pliegue extendido durante el ajuste cruzado?
   • From category summaries calculated using the other training folds
   • From summaries calculated using its own labels only
   • From summaries calculated using the final test labels
   • From summaries calculated using all training folds, including its own labels
3. Usando el ejemplo de suavizado de la guía, ¿qué valor resulta de una suma objetivo de 30, una cuenta de dos, una media global de seis y una ponderación anterior de cuatro?
   • 15
   • 6
   • 54
   • 9
4. ¿Por qué el suavizado arrastra la estimación de una categoría rara hacia la media de entrenamiento global?
   • To make every category's encoding identical
   • To reduce the influence of very small category samples
   • To remove the need for an evaluation split
   • To guarantee that category frequency cannot change
5. ¿Qué operación TargetEncoder de scikit-learn utiliza ajuste cruzado interno al codificar datos de entrenamiento?
   • Calling transform on training rows using a mapping fitted on all those rows
   • Calling fit and then transform on the same training rows
   • Calling fit_transform on the training data
   • Calling fit once before dividing the training data into evaluation folds
6. How should evaluation labels be used when constructing a target-encoding mapping?
   • They should not be used to learn the mapping being evaluated
   • They should define the mapping for rare categories only
   • They should determine the global mean but not category means
   • They should be added when the training score is low
7. According to the guide, what does scikit-learn's TargetEncoder use for a category absent from its fitting data?
   • The smallest observed category value
   • The held-out outcome for that row
   • A newly calculated test-set average
   • The learned target mean
8. A target-encoding dataset contains repeated entities and time-dependent outcomes. Which design needs attention beyond ordinary random cross-fitting?
   • Using random row folds because each row excludes its own label
   • Choosing splits that respect the intended group and time boundaries
   • Using stronger smoothing as a substitute for respecting time boundaries
   • Using an ordinary Pipeline as proof that its encoder's internal folds respect groups