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 Amazon reviews dataset


We use the Amazon product reviews polarity dataset for the various projects in this chapter. It is an open dataset constructed and made available by Xiang Zhang. It is used as a text classification benchmark in the paper: Character-level Convolutional Networks for Text Classification and Advances in Neural Information Processing Systems 28, Xiang Zhang, Junbo Zhao, Yann LeCun, (NIPS 2015).

The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, 4 and 5 as positive. Samples of score 3 are ignored. In the dataset, class 1 is the negative and class 2 is the positive. The dataset has 1,800,000 training samples and 200,000 testing samples.

The train.csv and test.csv files contains all the samples as comma-separated values. There are three columns in them, corresponding to class index (1 or 2), review title, and review text. The review title and text are escaped using double quotes ("), and any internal double quote...