XGBoost Algorithm Quiz

Answers and explanations appear as you take the quiz.

1. How does gradient boosting generally build its predictor?
   • By averaging independent trees with unchanged fitting targets
   • By replacing earlier trees entirely at every round
   • By adding learners sequentially to improve the current objective
   • By optimizing a single full-depth tree without additive updates
2. Which parameter pair captures the tradeoff between the size of each additive update and how many updates are made?
   • Maximum depth and minimum child weight
   • Learning rate and number of boosting rounds
   • Column subsampling and row subsampling
   • Leaf regularization and split-gain threshold
3. What role do complexity penalties and tree limits play?
   • They guarantee probabilities are calibrated on every population
   • They determine the ground-truth labels before fitting
   • They replace the need to measure held-out performance
   • They constrain model complexity and can reduce overfitting
4. Why does early stopping require a separate final test evaluation?
   • The monitored validation metric participates in choosing an iteration
   • Early stopping makes the validation set unused during selection
   • Stopping rounds should be chosen directly from final-test outcomes
   • A chosen stopping point guarantees performance on any later population
5. Which claim about XGBoost on tabular data is justified?
   • It always beats every baseline
   • It requires no data split
   • It can be a strong candidate, but performance depends on the task and evaluation
   • It guarantees production accuracy from a leaderboard result
6. Why record the library version and objective with a trained model?
   • The algorithm name alone fixes all defaults across releases
   • Behavior and available options can vary by version and task setup
   • An objective replaces the need to record the data split
   • Native missing-value support establishes what every missing value means
7. When are row or feature subsampling settings most defensibly chosen?
   • By assuming any subsampling improves accuracy
   • By selecting against final test labels
   • By setting them without considering data size
   • By measuring them within the validation protocol
8. At training and inference time, what should remain consistent about the model inputs?
   • Keep feature and missing-value conventions consistent with training
   • Assign new category codes independently after deployment
   • Recompute learned feature transformations separately for each inference batch without validation
   • Change the meaning of missing values while retaining the same model artifact