Book Image

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
Book Image

R Machine Learning Projects

By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
The Road Ahead

Building a text sentiment classifier with fastText

fastText is a library and is an extension of word2vec for word representation. It was created by the Facebook Research Team in 2016. While Word2vec and GloVe approaches treat words as the smallest unit to train on, fastText breaks words into several n-grams, that is, subwords. For example, the trigrams for the word apple are app, ppl, and ple. The word embedding for the word apple is sum of all the word n-grams. Due to the nature of the algorithm's embedding generation, fastText is more resource-intensive and takes additional time to train. Some of the advantages of fastText are as follows:

  • It generates better word embeddings for rare words (including misspelled words).
  • For out of vocabulary words, fastText can construct the vector for a word from its character n-grams, even if a word doesn't appear in training corpus...