Which selection represents a type of Classification Model that produces decision trees?

The selection that accurately represents a type of Classification Model that produces decision trees is Rule Induction. This method involves creating rules that can be used for classification tasks, and decision trees are a common outcome of applying rule induction techniques. Decision trees structure the classification process visually, allowing for easy interpretation of the rules derived from the training data.

In rule induction, the goal is to discover useful patterns in the data and transform these patterns into rules that can classify new observations. This contrasts with other methods, such as AutoCluster, which is focused on unsupervised clustering, or K-Means, which also deals with clustering but not classification. Traditional statistics may employ various methods for analysis, but it does not specifically denote the tree-like structure found in decision trees used for classifications. Thus, Rule Induction is the correct choice as it directly leads to the formation of decision trees used in classification tasks.