Predictive analytics: the power to predict who will click, buy, lie, or die
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
[2016]
|
| Ausgabe: | Revised and updated |
| Schlagworte: | |
| Online-Zugang: | FHD01 Inhaltsverzeichnis |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781119145684 9781119153658 |
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Datensatz im Suchindex
| _version_ | 1804175994311933952 |
|---|---|
| adam_text | Titel: Predictive analytics
Autor: Siegel, Eric
Jahr: 2016
Contents
Foreword Thomas H. Davenport
XVll
Preface to the Revised and Updated Edition
IVIiat s new and who s this book for—the Predictive
Analytics FAQ
XXI
Preface to the Original Edition
What is the occupational hazard of predictive analytics?
XXIX
Introduction
The Prediction Effect
1
How does predicting human behavior combat risk, fortify healthcare,
toughen crime fighting, boost sales, and cut costs? Wiy must a
computer learn in order to predict? How can lousy predictions be
extremely valuable? What makes data exceptionally exciting? How is
data science like porn? Why shouldn t computers be called computers?
Why do organizations predict when you will die?
Chapter 1
Liftoff! Prediction Takes Action (deployment) 23
How much guts does it take to deploy a predictive model into field
operation, and what do you stand to gain? What happens when a man
invests his entire life savings into his own predictive stock market
trading system?
XIII
XIV
CONTENTS
Chapter 2
With Power Comes Responsibility: Hewlett-Packard,
Target, the Cops, and the NSA Deduce Your Secrets (ethics) 47
How do we safely harness a predictive machine that can foresee job
resignation, pregnancy, and crime? Are civil liberties at risk? Why
does one leading health insurance company predict policyholder death?
Two extended sidebars reveal: I) Does the government undertake
fraud detection morefor its citizens or for self-preservation, and 2)for
what compelling purpose does the NSA need your data even if you
have no connection to crime whatsoever, and can the agency use
machine learning supercomputers to fight terrorism without endan-
gering human rights?
Chapter 3
The Data Effect: A Glut at the End of the Rainbow (data) 103
We are up to our ears in data, but how much can this raw material really
tell us? What actually makes it predictive? What are the most bizarre
discoveriesfrom data? When wefind an interestinginsight, why are we
often better off not asking why? In what way is bigger data more
dangerous? How do we avoid beingfooled by random noise and ensure
scientific discoveries are trustworthy?
Chapter 4
The Machine That Learns: A Look inside Chase s
Prediction of Mortgage Risk (modeling) 147
What form of risk has the pcfect disguise? How does prediction
transform risk to opportunity? What should all businesses learn from
insurance companies? Why does machine learning require art in
addition to science? What kind of predictive model can be understood
by everyone? How can we confidently trust a machine s predictions?
Why couldn t prediction prevent the global financial crisis?
Contents
xv
Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and
Supercharging Prediction (ensembles) 185
To crowdsource predictive analytics—outsource it to the public at
large—a company launches its strategy, data, and research discoveries
into the public spotlight. How can this possibly help the company
compete? What key innovation in predictive analytics has crowdsourc-
ing helped develop? Must supercharging predictive precision involve
overwhelming complexity, or is there an elegant solution? Is there
wisdom in nonhuman crowds?
Chapter 6
Watson and the Jeopardy! Challenge (question answering) 207
How does Watson—IBM s Jeopzrdyl-playing computer—work?
Why does it need predictive modeling in order to answer questions, and
what secret sauce empowers its high performance? How does the
iPhone s Siri compare? Why is human language such a challengefor
computers? Is artificial intelligence possible?
Chapter 7
Persuasion by the Numbers: How Telenor, U.S. Bank,
and the Obama Campaign Engineered Influence (uplift) 251
What is the scientific key to persuasion? Why does some marketing
fiercely backfire? Why is human behavior the wrong thing to predict?
What should all businesses leam about persuasion from presidential
campaigns? Wltat voter predictions helped Obama win in 2012 more
than the detection of swing voters? How could doctors killfewerpatients
inadvertently? How is a person like a quantum particle? Riddle:
What often happens to you that cannot be perceived and that you can t
even be sure has happened afterward—but that can be predicted in
advance?
xvi Contents
Afterword 291
Eleven Predictions for the First Hour of2022
Appendices
A. The Five Effects of Prediction 295
B. Twenty Applications of Predictive Analytics 296
C. Prediction People—Cast of Characters 300
Hands-On Guide 303
Resources for Further Learning
Acknowledgments 307
About the Author 311
Index 313
Also see the Central Tables (color insert) for a cross-industry compendium
of 182 examples of predictive analytics.
This book s Notes—120 pages of citations and comments pertaining to the
chapters above—are available online at ivww.PredictiveNotes.com.
|
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| spelling | Siegel, Eric 1968- (DE-588)1052902154 aut Predictive analytics the power to predict who will click, buy, lie, or die Eric Siegel Revised and updated Hoboken, New Jersey Wiley [2016] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Wirtschaftsentwicklung (DE-588)4066438-7 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Verhalten (DE-588)4062860-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Prognose (DE-588)4047390-9 s Wirtschaftsentwicklung (DE-588)4066438-7 s Verhalten (DE-588)4062860-7 s 1\p DE-604 Erscheint auch als Druck-Ausgabe, Paperback 978-1-119-14567-7 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028825333&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Siegel, Eric 1968- Predictive analytics the power to predict who will click, buy, lie, or die Wirtschaftsentwicklung (DE-588)4066438-7 gnd Prognose (DE-588)4047390-9 gnd Verhalten (DE-588)4062860-7 gnd Data Mining (DE-588)4428654-5 gnd |
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| title_auth | Predictive analytics the power to predict who will click, buy, lie, or die |
| title_exact_search | Predictive analytics the power to predict who will click, buy, lie, or die |
| title_full | Predictive analytics the power to predict who will click, buy, lie, or die Eric Siegel |
| title_fullStr | Predictive analytics the power to predict who will click, buy, lie, or die Eric Siegel |
| title_full_unstemmed | Predictive analytics the power to predict who will click, buy, lie, or die Eric Siegel |
| title_short | Predictive analytics |
| title_sort | predictive analytics the power to predict who will click buy lie or die |
| title_sub | the power to predict who will click, buy, lie, or die |
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