# Welcome to the Book Club "Hands-On Machine Learning with R" - Ch. 6: Regularized Regression

## Some house rules to make the meeting nice for everyone

- Please familiarize with our [code of conduct](https://rladies.org/code-of-conduct/). In summary, please be nice to each other and help us make an **inclusive** meeting! :purple_heart:
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### Links :link:

Book/organisation:
- [Book: "Hands-On Machine Learning with R"](https://bradleyboehmke.github.io/HOML/)
- [GitHub Repository](https://github.com/rladiesnl/book_club_handsonML)
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## Chapter 7: Regularized Regression and Chapter 8: K-Nearest Neighbors

### Registry :clipboard:
Name / pronouns / R level / place where you join from

- WRITE YOUR NAME AND INFO HERE! :point_left:
- Gerbrich / she, her / advanced / Utrecht
- Martine / she, her / advanced / 's-Hertogenbosch NL
- Ece / she, her / intermediate / Rotterdam, NL
- Ale / she, her / intermediate / Utrecht
- Oussama / He,Him / Advanced / Dijon, France
- Lill Eva / She, her / intermediate / Utrecht
- Soly / she, her / beginner-int / Helsinki, FIN
- Ona Marija / she, her / beginner / Hannover

### Do you have any questions? :question:
You can write them down here, and if you have answers to posted questions please go ahead, we are all learning together.

- What is the value "importance" (VIP) on the Y-axis and how is it calculated?

"Variable Importance Plots" is a way of visualizing the relative importance a variable has in the model. there are different ways to compute this, see also the VIP R package.

The Variance Inflation Factor (VIF) is a measure of the amount of multicollinearity in a regression model.

## About today's topic

### Any take-home messages you want to share?

Help others remember the main points you took of these chapters:

- WRITE YOUR TAKE HOME MESSAGES HERE! :point_left:
- Linear Regression has lot of assumptions, multicollinearity (covarying variables) is an issue.
- Bet on spasity principle: when large set of variables, most effects are not significant.
- Add penalty (increasing with nr of variables in the model) to minization of squared errors.
- With lasso penalty you can do automated feature selection
- Elastic net is a linear combi of ridge and lasso
- Optimal level of lambda: find it via cross-validation
- If you have very time consuming analyses in yout quarto/Rmd presentation, you can prerun them and save in a plot, and add the plot to the presentation instead of calculating it while compiling.
- Regularized regression also has assumptions, like monotonic linear relationships

### Do you have any interesting links regarding the topic? :link:

If you have suggestions of books/blog posts/articles, etc. that could help people getting further into the topic. Write them here:

- WRITE YOUR LINK HERE! :point_left:

### Feedback :left_speech_bubble:

Please help us get better at this by giving us some feedback :sparkles: Things you liked or things that could improve! :smile:

- WRITE YOUR FIRST COMMENT HERE! :point_left:

## Sign-up for presenting a chapter!

- Chp 7-8 - Elena Dudukina (7 nov)
- Chp 9-10 - Veerle (21 Nov)
- Chp 11 - Oussama (dec 5)
- Chp 12 - Ece (19 Dec)
- Chp 13 - ?
- Chp 14 - ?
- Chp 15 - Ece (TBD)
- Chp 16 - Brandon, co-author of the book (TBD)
- Chp 17 - Shweta (would try to) (TBD)
- Chp 18 - ?
- Chp 19 - ?
- Chp 20 - 21 -(22) - Martine (TBD)