Final answer:
Random Forest and Gradient Boosting Algorithm (GBM) are both tree-based algorithms used in machine learning, but they differ in their approach to combining predictions and building decision trees.
Step-by-step explanation:
Random Forest and Gradient Boosting Algorithm (GBM) are both tree-based algorithms used in machine learning. However, there are some key differences between the two.
1. Random Forest creates multiple decision trees and combines their predictions to make the final prediction. Each tree in the random forest is built independently, and the final prediction is determined by majority voting or averaging.
2. Gradient Boosting Algorithm builds decision trees iteratively, where each tree tries to correct the mistakes made by the previous tree. The final prediction is determined by combining the predictions of all the trees.
In summary, while both algorithms are tree-based, Random Forest combines the predictions of multiple independent decision trees, whereas Gradient Boosting Algorithm builds decision trees sequentially to correct mistakes.