Target Encoding Quiz

Answers and explanations appear as you take the quiz.

1. A category occurs in one training row. Why is using that row's unsmoothed target mean as its own input dangerous?
   • 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. How should a held-out fold receive target encodings during cross-fitting?
   • 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. Using the guide's smoothing example, what value results from a target sum of 30, count of two, global mean six and prior weight four?
   • 15
   • 6
   • 54
   • 9
4. Why does smoothing pull a rare category's estimate toward the global training mean?
   • 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. Which scikit-learn TargetEncoder operation uses internal cross-fitting when encoding training data?
   • 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