Search icon CANCEL
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
The Statistics and Machine Learning with R Workshop

You're reading from  The Statistics and Machine Learning with R Workshop

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Pages 516 pages
Edition 1st Edition
Languages
Author (1):
Liu Peng Liu Peng
Profile icon Liu Peng

Table of Contents (20) Chapters

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Penalized logistic regression

As the name suggests, a penalized logistic regression model includes an additional penalty term in the loss function of the usual logistic regression model. Recall that a standard logistic regression model seeks to minimize the negative log-likelihood function (or equivalently, maximize the log-likelihood function), defined as follows:

Q(𝜷) =  1 _ N   i=1 N  [ y i logp i + (1 y i)log(1 p i)]

Here, p i =  1 _ 1 + e (β 0+β 1x 1 (i)+β 2x 2 (i)++β px p (i)) is the predicted probability for input x (i), y i is the corresponding target label, and 𝜷 = { β 0, β 1, , β p} are model parameters to be estimated. Note that we now express the loss as a function of the coefficient vector as it...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}