Abstract
This chapter delves into L1 and L2 regularization techniques within the context of
linear regression, focusing on minimizing overfitting risks while maintaining a concise
presentation of mathematical theories. We explore these techniques through a concrete
numerical example with a small dataset for predicting house sale prices, providing a step-by-step walkthrough of the process. To further enhance comprehension, we supply sample codes
and draw comparisons with the Lasso and Ridge models implemented in the scikit-learn library.
By the end of this chapter, readers will acquire a well-rounded understanding of L1 and L2
regularization in the context of linear regression, their implications on model implementation
and performance, and be equipped with the knowledge to apply these methods in practical use.