Final answer:
Option 1). K-means clustering is the correct answer because it is an unsupervised learning algorithm, in contrast to Naive Bayes, Decision Trees, and Support Vector Machines, which are all supervised learning models.
Step-by-step explanation:
The question asks to identify which of the listed options is not a supervised learning model. Models are trained on labeled data in supervised learning, which means that every example in the training set is matched with the appropriate output. K-means clustering, Naive Bayes, Decision Trees, and Support Vector Machines are the available methods.
In reality, K-means clustering is an unsupervised learning algorithm that works with unlabeled data to perform clustering analysis. On the other hand, supervised learning models like Naive Bayes, Decision Trees, and Support Vector Machines are applied to classification and regression tasks using training data that has already been labeled.
Therefore, the answer is K-means clustering, which is not a supervised learning model. It is meant for grouping unlabeled data points into clusters based on their feature similarities, without guidance from an external teacher.