# 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.

Level up your interview prep. Join Educative to access 80+ hands-on prep courses.