What is the difference between linear and logistic regression?

Linear and logistic regressions are machine learning methods for solving different tasks:

• Linear regression is used for predicting continuous numerical values. The model builds a linear dependence between input features and the target variable.

• Logistic regression is applied to classification tasks, usually binary. It estimates the probability of an object belonging to one of the classes, using a logistic function (sigmoid) to transform a linear combination of features into a probability from 0 to 1.

Example:

• Linear regression: predicting the price of a house based on area and number of rooms.
• Logistic regression: determining whether an email is spam (yes/no).

The main difference lies in the target variable and activation function:

Characteristic	Linear Regression	Logistic Regression
Task type	Regression (number)	Classification (class labels)
Target variable	Continuous	Discrete (usually 0 or 1)
Activation function	Linear	Logistic (sigmoid)
Model output	Any number	Probability (0..1)