The main difference between supervised and unsupervised learning
The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.
The algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer in supervised learning. While supervised learning models are more accurate than unsupervised learning models, they do necessitate human interaction to properly identify the data. A supervised learning model, for example, can forecast the length of your commute based on the time of day, weather conditions, and other factors. But first, you’ll have to teach it that driving in the rain adds to the time it takes.
Unsupervised learning models, in contrast, work on their own to discover the inherent structure of unlabeled data. Note that they still require some human intervention for validating output variables. For example, an unsupervised learning model can identify that online shoppers often purchase groups of products at the same time. However, a data analyst would need to validate that it makes sense for a recommendation engine to group baby clothes with an order of diapers, applesauce and sippy cups.