Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Modeling using random forests

Random forests, also known as random decision forests, are a machine learning algorithm that comes from the family of ensemble learning algorithms. It is used for both regression and classification tasks. Random forests are nothing but a collection or ensemble of decision trees, hence the name.

The working of the algorithm can be described briefly as follows. At any point in time, each tree in the ensemble of decision trees is built from a bootstrap sample, which is basically sampling with replacement. This sampling is done on the training dataset. During the construction of the decision tree, the split which was earlier being chosen as the best split among all the features is not done anymore. Now the best split is always chosen from a random subset of the features each time. This introduction of randomness into the model increases the bias of the model slightly but decreases the variance of the model greatly which prevents the overfitting of models, which is...