What is regression in machine learning?

Regression is a type of supervised machine learning used to predict a continuous numerical value.

Instead of predicting a category such as spam or not spam, a regression model predicts a quantity.

Examples of regression problems

Regression can be used to predict values such as:

• house price
• monthly sales
• temperature
• delivery time
• customer lifetime value
• electricity consumption

For example:

Input: house size, location, number of rooms
Output: predicted house price = ₹72,00,000

How regression works

A regression model learns a relationship between input features and a numerical target from historical examples.

During training, it tries to find a function that produces predictions close to the known target values.

In simple form:

features → regression model → numerical prediction

Regression target

The target variable in a regression problem is usually continuous or ordered numerical data.

Examples include:

price = 250000
temperature = 31.5
delivery_time = 42 minutes

Regression vs classification

• Regression: predicts a numerical quantity, such as price or demand.
• Classification: predicts a category or class, such as fraud/not fraud or cat/dog.

The important question is what kind of output the problem requires.

Common regression algorithms

Examples include:

• linear regression
• Ridge regression
• Lasso regression
• decision tree regression
• random forest regression
• gradient-boosted regression models

Different algorithms can model different types of relationships between the features and target.

Regression predictions are not always exact

A regression model estimates the target based on patterns learned from data.

Its quality is evaluated by comparing predictions with actual values using regression metrics such as MAE, MSE, RMSE, or R².

These evaluation metrics are covered later in this section.

In short, regression is supervised learning for predicting numerical quantities. It learns from examples where the target is a number and estimates that number for new observations.