Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Mastering Predictive Analytics with R, Second Edition
  • Toc
  • feedback
Mastering Predictive Analytics with R, Second Edition

Mastering Predictive Analytics with R, Second Edition

By : James D. Miller , Rui Miguel Forte
5 (1)
close
Mastering Predictive Analytics with R, Second Edition

Mastering Predictive Analytics with R, Second Edition

5 (1)
By: James D. Miller , Rui Miguel Forte

Overview of this book

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.
Table of Contents (16 chapters)
close
8
8. Dimensionality Reduction
15
Index

Chapter 2. Tidying Data and Measuring Performance

In this chapter, we will cover the topics of tidying your data in preparation for predictive modeling, performance metrics, cross-validation, and learning curves.

In statistics, it is an accepted concept that there are two types of data, which are:

  • Untidy
  • Tidy

Untidy data is considered to be raw or messy; tidy data is data that has gone through a quality assurance process and is ready to be used.

bookmark search playlist download 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