Regression Isn’t Regularization: A Simple Guide to Understanding Both

Short summary

Regression models relationships to predict outcomes, while regularization modifies the learning process to control model complexity and prevent overfitting. Ridge regression shrinks coefficients using L2 penalties, Lasso can zero out coefficients for feature selection via L1 penalties, and Elastic Net combines both. The article provides basic formulas and a comparison table but lacks depth, code examples, or practical implementation guidance.

• •Regression predicts outcomes; regularization controls complexity to improve generalization
• •Ridge (L2) shrinks coefficients, Lasso (L1) can perform feature selection, Elastic Net combines both
• •Feature scaling matters because regularization penalties are sensitive to variable ranges

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