Book Image

Mastering Machine Learning with R - Third Edition

By : Cory Lesmeister
Book Image

Mastering Machine Learning with R - Third Edition

By: Cory Lesmeister

Overview of this book

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.
Table of Contents (16 chapters)

Modeling and evaluation

We'll start by mining the data for the overall association rules before moving on to our rules for beer specifically. Throughout the modeling process, we'll use the apriori algorithm, which is the appropriately named apriori() function in the arules package. The two main things that we'll need to specify in the function are the dataset and parameters. As for the parameters, you'll need to apply judgment when determining the minimum support, confidence, and the minimum and/or maximum length of basket items in an itemset. Using item frequency plots, along with trial and error, let's set the minimum support at 1 in 1,000 transactions and the minimum confidence at 90 %.

Additionally, let's establish the maximum number of items to be associated as 4. The following code creates the object that we'll call rules:

 rules <-
...