What is supervised learning?

Supervised learning is a type of machine learning in which a model learns from examples that include both the input data and the correct output.

Each training example contains:

• Features: the input information given to the model
• Label or target: the correct answer the model should learn to predict

The model learns the relationship between the features and the target so it can make predictions for new, unseen data.

Simple example

Suppose we want to predict whether an email is spam.

A training dataset might contain:

Email features → Spam
Email features → Not spam
Email features → Spam

Because the correct result is already known for each training example, the learning process is supervised.

After training, the model receives a new email and predicts whether it is spam based on the patterns it learned.

Common supervised learning problems

Supervised learning is commonly used for:

• Classification: predict a category, such as spam or not spam
• Regression: predict a numerical value, such as a house price

During training, the model's predictions are compared with the known targets. The learning algorithm adjusts the model to reduce the difference between its predictions and the correct answers.

In short, supervised learning trains a model using labeled examples so it can learn how to predict the correct output for new inputs.