Question

Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods?

1. Both methods can be used for classification task
2.Random Forest is use for classification whereas Gradient Boosting is use for regression task
3. Random Forest is use for regression whereas Gradient Boosting is use for Classification task
4. Both methods can be used for regression task
A) 1
B) 2
C) 3
D) 4
E) 1 and 4

Answer 1

Both Random Forest and Gradient Boosting can be used for classification and regression tasks. They are flexible ensemble methods that rely on decision tree-based classifiers. The correct answer to the question is E) 1 and 4.

Step-by-step explanation:

The question asks about the truths regarding Random Forest and Gradient Boosting ensemble methods. The correct answer is that both Random Forest and Gradient Boosting can be used for both classification and regression tasks. This makes options 1 ('Both methods can be used for classification task') and 4 ('Both methods can be used for regression task') correct. Therefore, the appropriate response is E) 1 and 4, indicating that both Random Forest and Gradient Boosting are versatile in their applications.

Random Forest applies a learning-based approach that involves creating multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. Gradient Boosting builds one tree at a time, and each new tree helps to correct errors made by previously trained trees. Both methods are based on decision tree classifiers, and while they have been traditionally used in their respective dominant domains (Random Forest for classification and Gradient Boosting for regression), they are indeed flexible and can be applied to both tasks.