Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Understanding the Jokes recommendation problem and the dataset


Dr. Ken Goldberg and his colleagues, Theresa Roeder, Dhruv Gupta, and Chris Perkins, introduced a dataset to the worldthrough their paper Eigentaste: A Constant Time Collaborative Filtering Algorithm, which is pretty popular in the recommender-systems domain. The dataset is named the Jester's jokes dataset. To create it, a number of users are presented with several jokes and they are asked to rate them. The ratings provided by the users for the various jokes formed the dataset. The data in this dataset is collected between April 1999 and May 2003. The following are the attributes of the dataset:

  • Over 11,000,000 ratings of 150 jokes from 79,681 users
  • Each row is a user (Row 1 = User #1)
  • Each column is a joke (Column 1 = Joke #1)
  • Ratings are given as real values from -10.00 to +10.00; -10 being the lowest possible rating and 10 being the highest
  • 99 corresponds to a null rating

The recommenderlab package in R provides a subset of this...