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Mastering Predictive Analytics with R - Second Edition by Rui Miguel Forte, James D. Miller

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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.

Getting started

Before we get started with discussing the process of tidying data, it would be very prudent to point out that whatever you do to tidy your data, you should be sure to:

  1. Create and save your scripts so that you can use them again for new or similar data sources. This is referred to as reusability ...

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