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

A

The learning rate determines how many boosting stages to perform

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

The method trains each weak learner sequentially

D

Weak learners are typically a shallow decision tree

登录即可查看完整答案

我们收录了全球超50000道真实原题与详细解析,现在登录,立即获得答案。

类似问题

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

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_ =================================================Early stopping triggers since the output of gbrt_best.n_estimators_ is less than 500The tree depth for each weaker learner is 5Performance improvement continues up to around boosting iteration 57, then performance plateaus

更多留学生实用工具

加入我们，立即解锁 海量真题 与 独家解析，让复习快人一步！