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
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

Arrow left icon
Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Choosing the Right Model for Your Use Case


So far, we have explored a set of white-box models and a couple of black-box machine learning models for the same classification use case. We also extended the same use case with a deep neural network in Keras and studied its performance. With the results from several models and various iterations, we need to decide which model would be the best for a classification use case. There isn't a simple and straightforward answer to this. In a more general sense, we can say that the best model would be a Random Forest or XGBoost for most use cases. However, this is not true for all types of data. There will be numerous scenarios where ensemble modeling may not be the right fit and a linear model would outperform it and vice versa. In most experiments conducted by data scientists for classification use cases, the approach would be an exploratory and iterative one. There is no one-size-fits-all model in machine learning. The process of designing and training...

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