What is Regression?

Regression is the machine learning approach for predicting numbers rather than categories. While classification asks 'which bucket does this belong to?', regression asks 'what value should this be?' When a real estate platform estimates a home's selling price or a financial model forecasts next quarter's revenue, regression is at work.

Linear regression is the simplest form, assuming a straight-line relationship between input features and the target value. For example, predicting a person's salary based on years of experience might fit a linear relationship reasonably well. Polynomial regression fits curves rather than straight lines. Ridge and Lasso regression add regularization to prevent overfitting. For complex, non-linear problems, neural networks and ensemble methods like gradient boosting are powerful regression tools.

Regression models are evaluated using error metrics that measure how far predictions deviate from actual values. Mean Absolute Error (MAE) measures average absolute deviation. Root Mean Squared Error (RMSE) penalizes large errors more heavily. R-squared measures how much of the variation in the target the model explains. The right metric depends on whether large errors are especially costly in your application.

Feature engineering is particularly important in regression. Creating the right input features - and understanding their relationship to the target - often matters more than choosing the right algorithm. A domain expert who understands what drives house prices can craft features that dramatically improve a price prediction model's accuracy.

Regression powers many business-critical AI applications. Demand forecasting helps retailers optimize inventory. Energy consumption predictions help utilities manage grid loads. Customer lifetime value models help companies prioritize marketing spend. Predictive analytics platforms often combine multiple regression models to forecast complex business outcomes.