Meaning
A machine learning approach that uses labeled data to train algorithms.
Definition
In supervised learning, models learn from input-output pairs, allowing them to predict outputs for new inputs. This method requires a dataset that includes the correct answers, or labels, for the model to learn from.
By understanding the relationship between the input data and the labels, the model can generalize this knowledge to make predictions on unseen data, making it a powerful tool for tasks like classification and regression.
Example
Email filtering systems use supervised learning to classify emails as spam or not based on labeled examples.
By analyzing features such as the sender’s address and the content of the email, the model learns to identify patterns that are indicative of spam, helping to keep inboxes clean.
