Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying R Machine Learning By Example
  • Table Of Contents Toc
  • Feedback & Rating feedback
R Machine Learning By Example

R Machine Learning By Example

By : Raghav Bali
4.6 (14)
close
close
R Machine Learning By Example

R Machine Learning By Example

4.6 (14)
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 (10 chapters)
close
close
9
Index

Model comparison and selection


We have explored various machine learning techniques and built several models to predict the credit ratings of customers, so now comes the question of which model we should select and how the models compare against each other. Our test data has 130 instances of customers with a bad credit rating (0) and 270 customers with a good credit rating (1).

If you remember, earlier we had talked about using domain knowledge and business requirements after doing modeling to interpret results and make decisions. Right now, our decision is to choose the best model to maximize profits and minimize losses for the German bank. Let us consider the following conditions:

  • If we incorrectly predict a customer with bad credit rating as good, the bank will end up losing the whole credit amount lent to him since he will default on the payment and so loss is 100%, which can be denoted as -1 for our ease of calculation.

  • If we correctly predict a customer with bad credit rating as bad,...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY