# ridge regression as SVD

(1.1 hours to learn)

## Summary

It's possible to write the ridge regression solution in terms of the SVD of the dataset. This gives insight into how it makes predictions. It also gives a way of defining the "degrees of freedom" or "effective number of parameters" of the model, which lets us analyze the degree of overfitting.

## Context

This concept has the prerequisites:

## Core resources (read/watch one of the following)

## -Free-

→ The Elements of Statistical Learning

A graudate-level statistical learning textbook with a focus on frequentist methods.

## -Paid-

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 7.5.3, pages 228-230

Other notes:

- If you don't know what PCA is, just think of it as the SVD.

## See also

-No Additional Notes-