Which of the following can be used for supervised learning?

The correct choice is Principal components because it is rooted in supervised learning techniques when used in the context of supervised dimensionality reduction. However, it’s important to clarify that while Principal Component Analysis (PCA) is predominantly an unsupervised learning method, it can be utilized in a supervised setting when the components are used as features in a model that predicts a target variable. This means that PCA can help in preprocessing data before applying a supervised learning algorithm, thus enhancing the model's performance by capturing the most informative features.

The other options, such as K-means clustering and Hierarchical clustering, are both examples of unsupervised learning techniques focused on identifying patterns or groupings in data without any associated target variable. These clustering methods do not rely on labeled outputs to guide their formation of clusters, making them unsuitable for supervised learning tasks. In summary, the utility of Principal components in providing feature extraction or transformation to inform a supervised learning model distinguishes it as the correct answer.