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Machine Learning with R
Machine Learning with R

Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems , Second Edition

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Machine Learning with R

Chapter 2. Managing and Understanding Data

A key early component of any machine learning project involves managing and understanding data. Although this may not be as gratifying as building and deploying models—the stages in which you begin to see the fruits of your labor—it is unwise to ignore this important preparatory work.

Any learning algorithm is only as good as its input data, and in many cases, the input data is complex, messy, and spread across multiple sources and formats. Because of this complexity, often the largest portion of effort invested in machine learning projects is spent on data preparation and exploration.

This chapter approaches these topics in three ways. The first section discusses the basic data structures R uses to store data. You will become very familiar with these structures as you create and manipulate datasets. The second section is practical, as it covers several functions that are useful to get data in and out of R. In the third section...

R data structures

There are numerous types of data structures across programming languages, each with strengths and weaknesses suited to particular tasks. Since R is a programming language used widely for statistical data analysis, the data structures it utilizes were designed with this type of work in mind.

The R data structures used most frequently in machine learning are vectors, factors, lists, arrays and matrices, and data frames. Each is tailored to a specific data management task, which makes it important to understand how they will interact in your R project. In the sections that follow, we will review their similarities and differences.

Vectors

The fundamental R data structure is the vector, which stores an ordered set of values called elements. A vector can contain any number of elements, but all of the elements must be of the same type of values. For instance, a vector cannot contain both numbers and text. To determine the type of vector v, use the typeof(v) command.

Several vector...

Managing data with R

One of the challenges faced while working with massive datasets involves gathering, preparing, and otherwise managing data from a variety of sources. Although we will cover data preparation, data cleaning, and data management in depth by working on real-world machine learning tasks in the later chapters, this section will highlight the basic functionality to get data into and out of R.

Saving, loading, and removing R data structures

When you have spent a lot of time getting a data frame into the desired form, you shouldn't need to recreate your work each time you restart your R session. To save a data structure to a file that can be reloaded later or transferred to another system, use the save() function. The save() function writes one or more R data structures to the location specified by the file parameter. R data files have an .RData extension.

Suppose you have three objects named x, y, and z that you would like to save in a permanent file. Regardless of whether...

Exploring and understanding data

After collecting data and loading it into R's data structures, the next step in the machine learning process involves examining the data in detail. It is during this step that you will begin to explore the data's features and examples, and realize the peculiarities that make your data unique. The better you understand your data, the better you will be able to match a machine learning model to your learning problem.

The best way to learn the process of data exploration is with an example. In this section, we will explore the usedcars.csv dataset, which contains actual data about used cars recently advertised for sale on a popular U.S. website.

Tip

The usedcars.csv dataset is available for download on the Packt Publishing support page for this book. If you are following along with the examples, be sure that this file has been downloaded and saved to your R working directory.

Since the dataset is stored in the CSV form, we can use the read.csv() function...

Summary

In this chapter, we learned about the basics of managing data in R. We started by taking an in-depth look at the structures used for storing various types of data. The foundational R data structure is the vector, which is extended and combined into more complex data types such as lists and data frames. The data frame is an R data structure that corresponds to the notion of a dataset, having both features and examples. R provides functions for reading and writing data frames to spreadsheet-like tabular data files.

We then explored a real-world dataset containing data on used car prices. We examined numeric variables using common summary statistics of center and spread, and visualized relationships between prices and odometer readings with a scatterplot. We examined nominal variables using tables. In examining the used car data, we followed an exploratory process that can be used to understand any dataset. These skills will be required for the other projects throughout this book.

Now...

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Description

Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

Who is this book for?

Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

What you will learn

  • Harness the power of R to build common machine learning algorithms with realworld data science applications
  • Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results
  • Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems
  • Classify your data with Bayesian and nearest neighbour methods
  • Predict values using R to build decision trees, rules, and support vector machines
  • Forecast numeric values with linear regression and model your data with neural networks
  • Evaluate and improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, and big data

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Publication date : Jul 31, 2015
Length: 452 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784393908
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Product Details

Publication date : Jul 31, 2015
Length: 452 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784393908
Category :
Languages :

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Table of Contents

13 Chapters
1. Introducing Machine Learning Chevron down icon Chevron up icon
2. Managing and Understanding Data Chevron down icon Chevron up icon
3. Lazy Learning – Classification Using Nearest Neighbors Chevron down icon Chevron up icon
4. Probabilistic Learning – Classification Using Naive Bayes Chevron down icon Chevron up icon
5. Divide and Conquer – Classification Using Decision Trees and Rules Chevron down icon Chevron up icon
6. Forecasting Numeric Data – Regression Methods Chevron down icon Chevron up icon
7. Black Box Methods – Neural Networks and Support Vector Machines Chevron down icon Chevron up icon
8. Finding Patterns – Market Basket Analysis Using Association Rules Chevron down icon Chevron up icon
9. Finding Groups of Data – Clustering with k-means Chevron down icon Chevron up icon
10. Evaluating Model Performance Chevron down icon Chevron up icon
11. Improving Model Performance Chevron down icon Chevron up icon
12. Specialized Machine Learning Topics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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(40 Ratings)
5 star 65%
4 star 25%
3 star 5%
2 star 2.5%
1 star 2.5%
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Rob Lawton Oct 03, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am pleased to have bought this book (directly from Packt, the publisher) based on positive reviews of the first edition. My background is as a SQL programmer and CRM data analyst, and although I had some experience of data mining algorithms in other software, I do not have a lot of prior experience in R.Before jumping into descriptions of the various data mining algorithms, there is some useful material on the basics of data handling in R, which was a useful refresher for me as an R novice.After this, the author describes clearly and concisely the use of the various algorithms, together with discussion on the strengths and weaknesses of each. There are examples given, using mostly real world data (which is available to download). These are easy to follow, giving enough detail to understand the concepts without getting bogged down in too much statistical detail.I found it useful to have some understanding of the concepts of some of the mining models, but this is not essential as the book gives a good grounding in both the concepts, and how to apply them in R.
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William C Scott Aug 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Amazing book. I can't recommend it enough. Within a day or so, you can be running a Machine Learning algorithm on some data. Lantz provides examples of several different approaches in ML (KNN, SVM, RNN, etc). And then depending on your problem, you can start playing around with the packages.
Amazon Verified review Amazon
Cesar A. Souza Feb 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very good book, with everything you need to start working with machine learning and R.
Amazon Verified review Amazon
Sudheer Rao Jul 12, 2016
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Very easy guide to learn the basic concept of machine learning
Amazon Verified review Amazon
Chris Papesh Sep 23, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Machine Learning with R-Second Edition" is an excellent introduction to building algorithms using R. Bert Lantz, the author, provides hands-on case studies combined with essential theory to understand basic to advanced methods. I served as an Assistant Vice President for Carnegie Mellon University and a Director with Oracle and PeopleSoft; I am confident that "Machine Learning with R" will provide a strong framework for new students, but also an excellent guide for working professionals learning how to use R. I found that the example code worked perfectly and that I could quickly build the case study models.My firm, Pacific Health Software-PHS, is building Open Source free Predictive Analytic (PA) software with PA models built upon Open Source clinical systems: OpenEMR; OpenMRS & VISTA. Initial free models (built in R) with links to OpenEMR clinical data-sets will be released in October 2015; http://phs-redmine.bitnamiapp.com/redmine/.We anticipate that models can bring higher than 90% to 98% prediction rates on the course to take with major diseases such as Heart Disease.We plan to work with the US Peace Corps to assist clinical decision making in Peace Corp clinics in Africa and Asia that use OpenEMR clinical systems; we will be asking a community of volunteers to build and distribute additional free PA models. We will recommend that community volunteers, developers, who are new to using R, buy "Machine Learning with R": to learn to build new models with R.
Amazon Verified review Amazon
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