Why does linear regression square its mistakes?

Positive and negative errors can cancel—but that’s only part of the story. Through visual simulations, we explore how squaring errors makes large mistakes more expensive, creates a smooth optimization landscape, and makes regression sensitive to outliers.

In this video:

• Why adding signed errors fails
• How least squares finds the best-fitting line
• What the error squares actually represent
• Squared error versus absolute error
• Why one outlier can pull the regression line

Change how you measure a mistake, and you can change the answer a machine finds.

#LinearRegression #MachineLearning #LeastSquares