Final answer:
Both Random Forest and Gradient Boosting can be used for both classification and regression tasks. They use decision trees in ensemble methods to improve prediction accuracy and handle complex datasets effectively.
Step-by-step explanation:
The correct answer to the question about Random Forest and Gradient Boosting ensemble methods is that both statements 1 and 4 are true. Random Forest and Gradient Boosting are versatile algorithms that can be applied to both classification and regression tasks. Although they both utilize decision trees as their base learners, they employ different strategies for learning. Random Forest builds many decision trees and merges them together to get a more accurate and stable prediction. In contrast, Gradient Boosting builds one tree at a time, and each new tree helps to correct errors made by previously trained trees. This learning-based approach allows these ensemble methods to deal with complex datasets effectively.
Regarding the specifics of each ensemble method, Random Forest is an algorithm that builds a large number of decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. On the other hand, Gradient Boosting builds trees sequentially, with each tree trying to correct the mistakes of the previous one, and can be used for both regression and classification tasks as well. These methods are examples of ensemble learning where multiple models are used to improve the robustness and accuracy of predictions. Their ability to handle various data types and their robust nature make them popular choices in the field of machine learning.