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

Summary


In this chapter, we reviewed two classification techniques: KNN and SVM. The goal was to discover how these techniques work and ascertain the differences between them, by building and comparing models on a common dataset. KNN involved both unweighted and weighted nearest neighbor algorithms, and for SVM, only a linear model was developed, which outperformed all other models.

We examined how to use Recursive Feature Elimination to find an optimal set of features for both methods. We used the extremely versatile caret package to train the models. We expanded our exploration of model performance using a confusion matrix, and the relevant statistics that one can derive from the matrix. We'll now use tree-based classifiers, which are very powerful and very popular.