Regression Algorithms

Introduction

Regression is a supervised learning task that predicts a continuous numeric value, such as a house price, temperature, or demand forecast. The model learns the relationship between input features and a numeric target from labeled data. It answers how much or how many rather than which category.

Definition

Regression algorithms learn the relationship between input features and a continuous target variable.

Types

Linear Regression

Models linear relationships between variables

Polynomial Regression

Models non-linear relationships using polynomial functions

Ridge Regression

Linear regression with L2 regularization

Lasso Regression

Linear regression with L1 regularization

Use Cases

• House price prediction
• Sales forecasting
• Temperature prediction
• Stock price analysis
• Demand forecasting

Implementation

Regression models are evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

In Practice

Linear regression is the simplest form, while polynomial, ridge, lasso, and tree-based regressors handle more complex relationships. Regression models are evaluated with error metrics like mean absolute error, mean squared error, and the R-squared score that measures explained variance.

Key Points

• Predicts continuous values
• Can model complex relationships
• Feature scaling is often important
• Regularization helps prevent overfitting

References

• Regression Analysis Guide — Detailed guide to regression algorithms and techniques