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 support vector machines

Support vector machines belong to the family of supervised machine learning algorithms used for both classification and regression. Considering our binary classification problem, unlike logistic regression, the SVM algorithm will build a model around the training data in such a way that the training data points belonging to different classes are separated by a clear gap, which is optimized such that the distance of separation is the maximum. The samples on the margins are typically called the support vectors. The middle of the margin which separates the two classes is called the optimal separating hyperplane.

Data points on the wrong side of the margin are weighed down to reduce their influence and this is called the soft margin compared to the hard margins of separation we discussed earlier. SVM classifiers can be simple linear classifiers where the data points can be linearly separated. However, if we are dealing with data consisting of several features...