Which of the following statistical learning tools is/are examples of supervised learning?

Supervised learning refers to a class of machine learning tasks where a model is trained on a labeled dataset, meaning that the input data is associated with corresponding output labels. This type of learning seeks to map input variables to the output variable based on these labels.

Logistic regression is a fundamental example of supervised learning as it involves training a model to predict a binary outcome based on one or more predictor variables. The training data includes both the input data and the corresponding labels, enabling the model to learn the relationship between the variables.

Ridge regression, a regularization approach used in linear regression, is also a supervised learning technique. Similar to logistic regression, ridge regression requires labeled data to train the model and helps in preventing overfitting by adding a penalty to the size of the coefficients.

Cluster analysis, on the other hand, belongs to the category of unsupervised learning because it does not use labeled output. Instead, it seeks to group data points into clusters based on their similarities without predefined categories.

Thus, the correct answer identifies logistic regression and ridge regression as examples of supervised learning, reinforcing the understanding of how these techniques operate within the supervised learning framework.