Supervised Learning
Training a system using examples where the correct answer is already known, so it learns to map inputs to labels.
Plain English Explanation
Supervised learning means training a system using examples where the correct answer is already known. The system learns the relationship between inputs and labels, then tries to apply that relationship to new, unseen inputs.
It is the most common approach when you have clearly defined outcomes to predict.
Analogy
Supervised learning is like studying for an exam using a textbook full of questions and official answers. Once you understand the pattern, you can attempt new questions you have never seen before.
How is it used?
Doctors train diagnostic tools by feeding them thousands of X-rays already labelled as healthy or diseased. Customer service teams train chatbots using past conversations marked as good or bad responses.
Real-world Example
Spam filters, house price estimators, and disease classifiers are classic supervised learning applications.
Common Misconceptions
Supervised learning only works as well as the labels you provide — noisy, biased, or incomplete labels produce unreliable models.