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


A weak learner is an algorithm that performs relatively poorly—generally, the accuracy obtained with the weak learners is just above chance. It is often, if not always, observed that weak learners are computationally simple. Decision stumps or 1R algorithms are some examples of weak learners. Boosting converts weak learners into strong learners. This essentially means that boosting is not an algorithm that does the predictions, but it works with an underlying weak ML algorithm to get better performance.

A boosting model is a sequence of models learned on subsets of data similar to that of the bagging ensembling technique. The difference is in the creation of the subsets of data. Unlike bagging, all the subsets of data used for model training are not created prior to the start of the training. Rather, boosting builds a first model with an ML algorithm that...