What Does Supervised Learning Mean?
Supervised learning is an approach to machine learning (ML) that uses labeled datasets and correct outputs to train learning algorithms how to classify data or predict an outcome.
Supervised learning is useful for grouping data into specific categories (classification) and understanding the relationship between variables in order to make predictions (regression).
It is used to provide product recommendations, segment customers based on customer data, diagnose disease based on previous symptoms and perform many other tasks.
Techopedia Explains Supervised Learning
Supervised learning enables machines to classify objects, problems or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications.
During supervised learning, a machine is given data, known as training data in data mining parlance, based on which the machine does classification. For example, if a system is required to classify fruit, it would be given training data such as color, shapes, dimension and size. Based on this data, it would be able to classify fruit.
Usually a system requires multiple iterations of such process to be able to perform accurate classification. Since real-life classifications such as credit card fraud detection and disease classification are complex tasks, the machines need appropriate data and several iterations of learning sessions to achieve reasonable abilities.