Sum of Squared Errors (SSE) › Introduction

SSE is one of the most important concepts in regression analysis because it directly measures prediction accuracy and forms the mathematical foundation for finding the best-fitting line in linear regression. When we fit a regression model, we're essentially trying to minimize SSE - finding the parameters that make the sum of squared differences between our predictions and actual values as small as possible.

The concept of SSE extends beyond simple linear regression to multiple regression, machine learning algorithms, and model evaluation across the entire data science workflow. Understanding SSE helps interpret model performance, compare different models, and explains why certain optimization algorithms behave as they do.