# Regularization and Ensembles

Week 05, Fall 2023

**Start:**Monday, September 18**End:**Friday, September 22

## Summary

This week we will continue discussing the **supervised** learning **regression** task, but we will introduce *extensions* of methods that we have already seen. First we will look at using **regularization** to improve linear models. Specifically, we’ll looke at **lasso**, **ridge**, and **elastic net** regression. Next, we’ll introduce the notion of an **ensemble method**. In particular, we’ll use the decision trees we already know as the base learners for **random forests** and **boosted** models.

## Learning Objectives

After completing this week, you are expected to be able to:

*Understand*how the ridge and lasso constraints lead to shrunken and spare estimates.*Use*ridge regression to perform regression.*Use*lasso to perform regression.*Understand*how averaging the predictions from many trees (for example a random forest) can improve model performance.*Use*a random forest to perform regression.*Use*boosting to perform regression.

## Reading

Coming soon!

## Video

Head to **ClassTranscribe** to watch lecture recordings. They are arranged by date in the Lecture Capture Recordings playlist.

## Assignments

Coming soon!

## Office Hours

Staff | Day | Time | Location |
---|---|---|---|

David | Monday | 11:00 AM - 12:00 PM | 2328 Siebel Center |

Lahari | Wednesday | 4:00 PM - 5:00 PM | Siebel Center, Second Floor [ Queue ] |

David | Wednesday | 5:00 PM - 6:00 PM | Zoom |

Eunice | Thursday | 3:00 PM - 4:00 PM | Siebel Center, Second Floor [ Queue ] |