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 logistic regression

Logistic regression is a type of regression model where the dependent or class variable is not continuous but categorical, just as in our case, credit rating is the dependent variable with two classes. In principle, logistic regression is usually perceived as a special case of the family of generalized linear models. This model functions by trying to find out the relationship between the class variable and the other independent feature variables by estimating probabilities. It uses the logistic or sigmoid function for estimating these probabilities. Logistic regression does not predict classes directly but the probability of the outcome. For our model, since we are dealing with a binary classification problem, we will be dealing with binomial logistic regression.

First we will load the library dependencies as follows and separate the testing feature and class variables:

library(caret) # model training and evaluation
library(ROCR) # model evaluation