Which of the following is an example of an ensemble method?

An ensemble method refers to a technique that combines multiple individual models to produce a single, more accurate predictive model. Random forests exemplify this concept by constructing a multitude of decision trees during training and outputting the mode of their predictions for classification tasks or the average for regression tasks. This aggregation of predictions helps to reduce overfitting and enhances model robustness, ultimately improving performance on unseen data.

In contrast, the other choices represent different modeling techniques that do not combine multiple models. Linear regression and support vector machines are single model methodologies, focusing on finding a linear relationship or decision boundary, respectively. K-means clustering is primarily an unsupervised learning algorithm used for partitioning data into clusters based on similarity, without any ensemble approach incorporated within its framework.