Supervised learning is an important method in machine learning. It helps computers learn how to make predictions using labeled data. This means that the data we use to teach the computer already has the answers. The main goal is to use this data to guess what will happen next based on different features. There are two main types of supervised learning: classification and regression.

Classification

Classification is all about sorting data into different groups or categories.

For instance, think about how an email system decides if a message is "spam" or "not spam." The computer learns from emails that have already been labeled. Over time, it gets better at sorting new emails it hasn’t seen before.

Examples:

• Binary Classification: Figuring out if tumors are "malignant" (cancerous) or "benign" (not cancerous).
• Multiclass Classification: Recognizing numbers from 0 to 9 in handwritten notes.

Regression

Regression is used to predict ongoing outcomes. It looks at the relationship between different features and a number.

A typical example is predicting house prices based on factors like size, location, and how many rooms it has.

Examples:

• Predicting how much money a store will make next year based on the sales data from previous years.
• Estimating how warm it will be on a specific day by checking past weather reports.

In short, supervised learning uses both classification and regression. This helps machines make smart choices and predictions by learning from past data.