Question at position 7 Which of the following is False about Gradient Boosting Machines?The method trains each weak learner sequentiallyThe method involves computing the negative gradient of the loss function, and fit a weak learner to the negative gradient of the lossWeak learners are typically a shallow decision treeThe learning rate determines how many boosting stages to performSingle choice

A

The method trains each weak learner sequentially

B

The method involves computing the negative gradient of the loss function, and fit a weak learner to the negative gradient of the loss

C

Weak learners are typically a shallow decision tree

D

The learning rate determines how many boosting stages to perform

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Question at position 8 You use the following code to perform gradient boosting. The output of the last command gbrt_best.n_estimators_ gives 67. Which of the following is False? ================================================= # Initialization gbrt_best = GradientBoostingRegressor( max_depth=2, n_estimators = 500, learning_rate = 0.05, n_iter_no_change = 10, random_state = 42) # Training gbrt_best.fit(X,y) # Checking gbrt_best.n_estimators_ =================================================The tree depth for each weaker learner is 5Early stopping triggers since the output of gbrt_best.n_estimators_ is less than 500Performance improvement continues up to around boosting iteration 57, then performance plateaus

Question at position 7 Which of the following is False about Gradient Boosting Machines?The method trains each weak learner sequentiallyThe method involves computing the negative gradient of the loss function, and fit a weak learner to the negative gradient of the lossThe learning rate determines how many boosting stages to performWeak learners are typically a shallow decision tree

Question at position 8 You use the following code to perform gradient boosting. The output of the last command gbrt_best.n_estimators_ gives 67. Which of the following is False? ================================================= # Initialization gbrt_best = GradientBoostingRegressor( max_depth=2, n_estimators = 500, learning_rate = 0.05, n_iter_no_change = 10, random_state = 42) # Training gbrt_best.fit(X,y) # Checking gbrt_best.n_estimators_ =================================================Performance improvement continues up to around boosting iteration 57, then performance plateausEarly stopping triggers since the output of gbrt_best.n_estimators_ is less than 500The tree depth for each weaker learner is 5

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