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Mastering Predictive Analytics with R, Second Edition
Mastering Predictive Analytics with R, Second Edition

Mastering Predictive Analytics with R, Second Edition: Machine learning techniques for advanced models , Second Edition

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Profile Icon James D. Miller Profile Icon Rui Miguel Forte
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Arrow left icon
Profile Icon James D. Miller Profile Icon Rui Miguel Forte
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$54.99
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Paperback Aug 2017 448 pages 2nd Edition
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Mastering Predictive Analytics with R, Second Edition

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. Why spend time recreating the same code, rules, or logic if you don't have to? This applies to new data within the same project (that the scripts were developed for) or new projects you may be involved with in the future.
  2. Tidy your data as "far upstream" as possible, perhaps even at the original source. In other words, save and maintain the original data, but use programmatic scripts to clean it, fix mistakes, and save that cleaned dataset for further analysis.

Tidying data

It is worth clarifying what the idea of tidying data means. Tidying data is the process of reorganizing (or perhaps just organizing) data, as well as addressing perceived issues or concerns someone has identified within your data. Issues affect the quality of data. Data quality, of course, is relative to the proposed purpose of use (of the data).

Categorizing data quality

It is perhaps an accepted notion that issues with data quality may be categorized into one of the following areas:

  • Accuracy
  • Completeness
  • Update status
  • Relevance
  • Consistency (across sources)
  • Reliability
  • Appropriateness
  • Accessibility

The quality or level of quality of your data can be affected by the way it is entered, stored, and managed. The process of addressing data quality (referred to most often as data quality assurance (DQA)) requires a routine and regular review and evaluation of the data and performing ongoing processes termed profiling and scrubbing (this is vital even if the data is stored in multiple disparate systems, making these processes difficult).

Here, tidying the data will be much more project centric in that we're probably not concerned with creating a formal DQA process, but are only concerned with making certain that the data is correct for your particular predictive project.

In statistics, data unobserved or not yet reviewed by the data scientist...

Performance metrics

In the previous chapter, where we talked about the predictive modeling process, we delved into the importance of assessing a trained model's performance using training and test datasets. In this section, we will look at specific measures of performance that we will frequently encounter when describing the predictive accuracy of different models. It turns out that depending on the class of the problem, we will need to use slightly different ways of assessing (the model's) performance. As we focus on supervised models in this book, we will look at how to assess regression models and classification models. For classification models, we will also discuss some additional metrics used for the binary classification task, which is a very important and frequently encountered type of problem.

Note

Note: In statistics, the term performance is usually interchangeable with accuracy.

Assessing regression models

In a regression scenario, let's recall that through our model...

Cross-validation

Cross-validation (which you may hear some data scientists refer to as rotation estimation, or simply a general technique for assessing models), is another method for assessing a model's performance (or its accuracy).

Mainly used with predictive modeling to estimate how accurately a model might perform in practice, one might see cross-validation used to check how a model will potentially generalize; in other words, how the model will apply what it infers from samples, to an entire population (or dataset).

With cross-validation, you identify a (known) dataset as your validation dataset on which training is run, along with a dataset of unknown data (or first seen data) against which the model will be tested (this is known as your testing dataset). The objective is to ensure that problems such as overfitting (allowing non-inclusive information to influence results) are controlled, as well as provide an insight on how the model will generalize a real problem or on a real...

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. Why spend time recreating the same code, rules, or logic if you don't have to? This applies to new data within the same project (that the scripts were developed for) or new projects you may be involved with in the future.

  2. Tidy your data as "far upstream" as possible, perhaps even at the original source. In other words, save and maintain the original data, but use programmatic scripts to clean it, fix mistakes, and save that cleaned dataset for further analysis.

Tidying data


It is worth clarifying what the idea of tidying data means. Tidying data is the process of reorganizing (or perhaps just organizing) data, as well as addressing perceived issues or concerns someone has identified within your data. Issues affect the quality of data. Data quality, of course, is relative to the proposed purpose of use (of the data).

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Key benefits

  • • Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding
  • • Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types
  • • Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily

Description

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.

Who is this book for?

Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.

What you will learn

  • • Master the steps involved in the predictive modeling process
  • • Grow your expertise in using R and its diverse range of packages
  • • Learn how to classify predictive models and distinguish which models are suitable for a particular problem
  • • Understand steps for tidying data and improving the performing metrics
  • • Recognize the assumptions, strengths, and weaknesses of a predictive model
  • • Understand how and why each predictive model works in R
  • • Select appropriate metrics to assess the performance of different types of predictive model
  • • Explore word embedding and recurrent neural networks in R
  • • Train models in R that can work on very large datasets
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Table of Contents

15 Chapters
1. Gearing Up for Predictive Modeling Chevron down icon Chevron up icon
2. Tidying Data and Measuring Performance Chevron down icon Chevron up icon
3. Linear Regression Chevron down icon Chevron up icon
4. Generalized Linear Models Chevron down icon Chevron up icon
5. Neural Networks Chevron down icon Chevron up icon
6. Support Vector Machines Chevron down icon Chevron up icon
7. Tree-Based Methods Chevron down icon Chevron up icon
8. Dimensionality Reduction Chevron down icon Chevron up icon
9. Ensemble Methods Chevron down icon Chevron up icon
10. Probabilistic Graphical Models Chevron down icon Chevron up icon
11. Topic Modeling Chevron down icon Chevron up icon
12. Recommendation Systems Chevron down icon Chevron up icon
13. Scaling Up Chevron down icon Chevron up icon
14. Deep Learning Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Amazon Customer Dec 31, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides hands on learning experience. If you want to master the theoretical underpinnings of predictive modeling, then this is not for you. Rather, if you want to able to build predictive models right from day 1 regardless of your background, then I strongly recommend this book. This is the best book on predictive modeling in R so far.
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