Machine learning regression is a powerful technique used to predict continuous values. In this guide, we will explore the basics of regression and how it can be applied to real-world problems.
Types of Regression
Linear Regression
- The simplest form of regression.
- Predicts a linear relationship between the input variable and the output variable.
Logistic Regression
- Used for binary classification problems.
- Converts linear regression output to a probability.
Polynomial Regression
- Extends linear regression to polynomial functions.
- Allows for more complex relationships between input and output variables.
Applications
- Real Estate: Predicting house prices based on features like size, location, and number of bedrooms.
- Stock Market: Forecasting stock prices based on historical data and market trends.
Resources
For further reading on machine learning regression, we recommend checking out our comprehensive guide on Machine Learning Basics.
By understanding the different types of regression and their applications, you can unlock the power of machine learning to solve real-world problems.