Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

Arrow left icon
Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning FREE CHAPTER 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Model selection


We looked at five different models in examining this dataset. The following points were the test set error of these models:

  • Best subsets is 0.51
  • Ridge regression is 0.48
  • LASSO is 0.44
  • Elastic net is 0.48
  • LASSO with CV is 0.45

On a pure error, LASSO with seven features performed the best. However, does this best address the question that we are trying to answer? Perhaps the more parsimonious model that we found using CV with a lambda of ~0.125 is more appropriate. My inclination is to put forth the latter as it is more interpretable.

Having said all this, there is clearly a need for domain-specific knowledge from oncologists, urologists, and pathologists in order to understand what would make the most sense. There is that, but there is also the need for more data. With this sample size, the results can vary greatly just by changing the randomization seeds or creating different train and test sets (try it and see for yourself.) At the end of the day, these results may likely raise...

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 $19.99/month. Cancel anytime
Banner background image