You work as a machine learning specialist for a marketing firm. Your firm wishes to determine which customers in a dataset of its registered users will respond to a new proposed marketing campaign. You plan to use the XGBoost algorithm on the binary classification problem. In order to find the optimal model, you plan to run many hyperparameter tuning jobs to reach the best hyperparameter values. Which of the following hyperparameters must you use in your tuning jobs if your objective is set to multi:softprob? (Select TWO)

Answer options:

A.alpha
B.base_score
C.eta
D.num_round
E.gamma
F.num_class

Answers: D and F
Option A is incorrect. The alpha hyperparameter is used to adjust the L1 regulation term on weights. This term is optional. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Option B is incorrect. The base_score hyperparameter is used to set the initial prediction score of all instances. This term is optional. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Option C is incorrect. The eta hyperparameter is used to prevent overfitting. This term is optional. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Option D is correct. The num_round hyperparameter is used to set the number of rounds to run in your hyperparameter tuning jobs. This term is required. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Option E is incorrect. The gamma hyperparameter is used to set the minimum loss reduction required to make a further partition on a leaf node of the tree. This term is optional. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Option F is correct. This hyperparameter is used to set the number of classes. This term is required if the objective is set to multi:softmax or multi:softprob. (See the Amazon SageMaker developer guide titled XGBoost Hyperparameters)
Reference:
Please see the Amazon SageMaker developer guide titled Automatic Model Tuning and the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works.