Which of the following is a commonly used ensemble learning algorithm?

Ensemble learning algorithms combine multiple models to improve performance and increase the robustness of predictions. Random Forest is a specific type of ensemble method that utilizes a collection of decision trees, making it highly effective for both classification and regression tasks. The key to its strength lies in the way it builds each tree by using a random subset of features and data samples, which reduces overfitting and enhances the model's ability to generalize to unseen data.

Gradient Boosting is also an ensemble algorithm but is another approach altogether that builds trees sequentially, where each new tree attempts to correct errors from the previous ones. While it is a powerful method, it is different from Random Forest in its operational mechanics.

Support Vector Machine and K-Nearest Neighbors, while valuable algorithms, do not fall under the category of ensemble learning techniques. They operate as standalone classifiers without the ensemble strategy that combines multiple models for improved accuracy and performance. Therefore, Random Forest stands out as a prominent example of an ensemble learning algorithm.