Which of the following learning tools is an example of unsupervised learning?

Unsupervised learning involves techniques where the model learns patterns from data without any labeled outcomes. In other words, it analyzes input data without explicitly being told what to predict or classify.

Boosting, K-Nearest Neighbors, and Regression Trees are all examples of supervised learning. These methods rely on a set of training data that contains both input features and corresponding target labels. The model is trained to make predictions or classifications based on this provided data.

Boosting is an ensemble technique that combines the predictions of several base learners, focusing on those instances that were previously misclassified. K-Nearest Neighbors is a classification method that predicts labels for new instances by looking at the closest training examples. A Regression Tree predicts continuous outcomes based on the input features by partitioning the data into smaller subsets.

In contrast, unsupervised learning methods, such as clustering or dimensionality reduction techniques, operate on datasets without labeled outcomes, seeking to identify hidden structures or groupings within the data.

Since none of the listed options represent unsupervised learning, the correct response aligns with the understanding that none provide an example of an unsupervised learning tool.