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Practical Data Science with R 原版PDF by Zumel & Mount
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This book is about data science: a field that uses results from statistics, machine learning, and computer science to create predictive models. Because of the broad nature of data science, it’s important to discuss it a bit and to outline the approach we take in this book.
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MANNING
Nina Zumel
John Mount
F
OREWORD BY
Jim Porzak

Practical Data
Science with R
NINA ZUMEL
JOHN MOUNT
MANNING
S
HELTER
I
SLAND

For online information and ordering of this and other Manning books, please visit
www.manning.com. The publisher offers discounts on this book when ordered in quantity.
For more information, please contact
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Manning Publications Co.
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©2014 by Manning Publications Co. All rights reserved.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in
any form or by means electronic, mechanical, photocopying, or otherwise, without prior written
permission of the publisher.
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claimed as trademarks. Where those designations appear in the book, and Manning
Publications was aware of a trademark claim, the designations have been printed in initial caps
or all caps.
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the books we publish printed on acid-free paper, and we exert our best efforts to that end.
Recognizing also our responsibility to conserve the resources of our planet, Manning books
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Manning Publications Co. Development editor: Cynthia Kane
20 Baldwin Road Copyeditor: Benjamin Berg
PO Box 261 Proofreader: Katie Tennant
Shelter Island, NY 11964 Typesetter: Dottie Marsico
Cover designer: Marija Tudor
ISBN 9781617291562
Printed in the United States of America
1 2 3 4 5 6 7 8 9 10 – EBM – 19 18 17 16 15 14

vii
brief contents
P
ART
1 I
NTRODUCTION
TO
DATA
SCIENCE
.................................1
1
■
The data science process 3
2
■
Loading data into R 18
3
■
Exploring data 35
4
■
Managing data 64
P
ART
2 M
ODELING
METHODS
..................................................81
5
■
Choosing and evaluating models 83
6
■
Memorization methods 115
7
■
Linear and logistic regression 140
8
■
Unsupervised methods 175
9
■
Exploring advanced methods 211
P
ART
3 D
ELIVERING
RESULTS
. ...............................................253
10
■
Documentation and deployment 255
11
■
Producing effective presentations 287

ix
contents
preface xvii
about this book xix
P
ART
1 I
NTRODUCTION
TO
DATA
SCIENCE
......................1
1
The data science process 3
1.1 The roles in a data science project 3
Project roles 4
1.2 Stages of a data science project 6
Defining the goal 7
■
Data collection and management 8
Modeling 10
■
Model evaluation and critique 11
Presentation and documentation 13
■
Model deployment and
maintenance 14
1.3 Setting expectations 14
Determining lower and upper bounds on model performance 15
1.4 Summary 17
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