# Regularized Regression

Learn about the two techniques for handling overfitting in regression: Ridge and Lasso regression.

Overfitting is when the model gives low error on training data but a high error on testing data. You learned about overfitting in the previous lesson. A highly complex model leads to overfitting. In regression, overfitting refers to large values of coefficients. When the value of coefficients is very high, that coefficient dominates, leading to overfitting.

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