Differentiate between Supervised vs. Unsupervised Learning
Supervised Learning: In a supervised learning problem, there is an explicit, pre-defined target variable which has known labels. If the outcome is continuous, the problem is solved via a regression context. If the outcome has discrete categories, a classification approach is taken.
Unsupervised Learning: On the other hand, unsupervised learning is performed when the target labels are unknown. Thus, there is no explicit mapping from the feature space X to the target Y. In some cases, the target is unable to be measured precisely, or it can be unknown entirely. A common unsupervised learning task is clustering observations into categories based on their feature similarities and then deriving and interpreting class labels based on the shared feature values of each grouping.