Explanation
Imagine teaching a child to identify different types of fruit. You show them an apple and say, "This is an apple." You show them a banana and say, "This is a banana." You repeat this process with various fruits, always providing the correct label. Eventually, the child learns to identify each fruit on their own.
Supervised learning is similar. It's a type of machine learning where we train an algorithm on a labelled dataset. This means that each piece of data has a corresponding "correct answer" or label associated with it.
The algorithm learns to map the input data to the correct output label. Once trained, it can then predict the labels for new, unseen data.
Think of it as the algorithm attending a masterclass, with all the answers neatly provided. Its role is to pick up on patterns to ace the final exam.
The more examples the algorithm sees, the better it becomes at making accurate predictions.
Examples
Consumer Example
Consider your email spam filter. It's trained using supervised learning. You label emails as either "spam" or "not spam".
The algorithm learns to identify patterns in spam emails, such as specific words, phrases, or sender addresses.
When a new email arrives, the algorithm analyses its content and predicts whether it's spam or not, directing it to the appropriate folder.
It's like having a vigilant gatekeeper that protects your inbox from unwanted junk mail.
Business Example
Imagine a bank wanting to predict whether a customer will default on a loan. Using supervised learning, the bank can analyse historical data on past loan applications, including factors like credit score, income, and employment history.
The algorithm learns to identify the characteristics of customers who are likely to default.
When a new loan application is received, the algorithm can assess the risk of default and help the bank make informed lending decisions.
It's like having a financial oracle that can foresee potential risks.