Linear regression is a powerful statistical tool for data analysis and machine learning. But when your hypothesis (model) uses a higher order polynomial, your model could overfit the data. One way to avoid such ovefitting is by using Ridge Regression, or L2 Regularization. It effectively adds a term to the cost function that limits the models parameters values. This is also sometimes referred to as shrinkage.

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