Linear regression is a fundamental concept in machine learning, used for predicting continuous values. In this tutorial, we will delve into the basics of linear regression, its application, and how it works.
Basic Concept
Linear regression aims to model the relationship between a dependent variable and one or more independent variables. The simplest form of linear regression is called simple linear regression, which involves only one independent variable.
Equation
The equation for simple linear regression is:
$$ y = \beta_0 + \beta_1x $$
Where:
- ( y ) is the dependent variable.
- ( x ) is the independent variable.
- ( \beta_0 ) is the intercept.
- ( \beta_1 ) is the slope.
Application
Linear regression is widely used in various fields, such as:
- Economics: Predicting stock prices, economic growth, etc.
- Healthcare: Predicting patient outcomes, disease progression, etc.
- Marketing: Predicting customer behavior, sales, etc.
How It Works
Linear regression works by finding the best fit line (also known as the regression line) that minimizes the distance between the predicted values and the actual values.
Gradient Descent
One of the most common methods to find the best fit line is gradient descent. It iteratively adjusts the parameters (( \beta_0 ) and ( \beta_1 )) to minimize the cost function, which is the sum of the squared differences between the predicted and actual values.
Resources
For further reading, you can check out our Machine Learning Basics section.