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7 Modules
What does XGBoost stand for?
eXtreme Gradient Boosting
eXtra Gradient Boosting
Extreme Gradient Boosting
eXtreme Generalized Boosting
Which of the following is a key feature of XGBoost?
Regularization
Support for missing values
Built-in cross-validation
Automatic hyperparameter tuning
In XGBoost, what does the term 'booster' refer to?
The algorithm used to create the model
The individual weak learners
The final ensemble model
The process of boosting itself
Which of the following loss functions can be used with XGBoost?
Logistic loss
Squared loss
Hinge loss
Poisson loss
What is the purpose of the 'learning rate' parameter in XGBoost?
To control the contribution of each tree
To determine the maximum depth of the trees
To set the number of boosting rounds
To regularize the model
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7 Modules