Sum of Squared Errors (SSE) › Sum of Squared Errors (SSE): Measuring Model Performance
Michael BrenndoerferApril 22, 20251 min read
Machine Learning from ScratchAll chapters Part 3 of 9Foundations
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The Sum of Squared Errors (SSE) is a basic metric in regression analysis that quantifies how well a model fits the data by measuring the total squared differences between observed and predicted values. SSE is the basis for many other important metrics in data science and machine learning, including R-squared, mean squared error, and the optimization objective for least squares regression.