Index
A
- Abbott, Dean
- AB testing
- Accident Fund Insurance
- accommodation bookings
- actuarial approach
- advertisement targeting, predictive
- advertising. See marketing and advertising
- Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (Harcourt)
- AI. See artificial intelligence (AI)
- Airbnb
- Air Force
- airlines and aviation, predicting in
- Albee, Edward
- Albrecht, Katherine
- algorithmic trading. See blackbox trading
- Allen, Woody
- Allstate
- Amazon.com
- employee security access needs
- machine learning and predictive models
- Mechanical Turk
- personalized recommendations
- sarcasm in reviews
- American Civil Liberties Union (ACLU)
- American Public University System
- analytics
- Analytics Revolution, The (Franks)
- Ansari X Prize
- Apollo
- Apple, Inc.
- Apple Mac
- Apple Siri
- Applied Predictive Analysis (Abbott)
- “Are Orange Cars Really not Lemons?” (Elder, Bullard)
- Argonne National Laboratory
- Arizona Petrified Forest National Park
- Arizona State University
- artificial intelligence (AI)
- Amazon.com Mechanical Turk
- mind-reading technology
- possibility of, the
- Watson computer and
- Asimov, Isaac
- astronomy
- AT&T Research BellKor Netflix Prize teams
- Australia
- Austria
- automatic suspect discovery (ASD)
- approach to
- arguments for and against
- assumptions about NSA's use of
- challenges of
- defined
- example patterns
- privacy issues and
- automobile insurance
- crashes, predicting
- credit scores and accidents
- driver inattentiveness
- fraud predictions for
- Averitt
- aviation incidents
- Aviva Insurance (U.K.)
- AWK computer language
- Ayres, Ian
B
- backtesting. See also test data
- Baesens, Bart
- bagging (bootstrap aggregating)
- Baker, Stephen
- Baltimore
- Bangladesh
- Barbie dolls
- Bari, Anasse
- Bayes, Thomas (Bayes Network)
- Beane, Billy
- Beano
- Beaux, Alex
- behavioral predictors
- Bella Pictures
- BellKor
- BellKor Netflix Prize teams
- Ben Gurion University (Israel)
- Bernstein, Peter
- Berra, Yogi
- Berry, J. A.
- Big Bang theory
- Big Bang Theory, The (TV show)
- Big Brother
- BigChaos team
- big data
- about
- The Data Effect and
- “wider” data and
- billing errors, predicting
- black-box trading
- Black Swan, The (Taleb)
- Blue Cross Blue Shield of Tennessee
- BMW
- BNSF Railway
- board games, predictive play of
- Bohr, Niels
- Boire, Richard
- Bollen, Johan
- books, about Predictive Analytics
- book titles, testing
- Bowie, David
- brain activity, predicting
- Brandeis, Louis
- Brazil
- breast cancer, predicting
- Breiman, Leo
- Brigham Young University
- British Broadcasting Corporation
- Brobst, Stephen
- Brooks, Mel
- Brynjolfsson, Eric
- buildings, predicting fault in
- Bullard, Ben
- burglaries, predicting
- business rules, decision trees and
- buying behavior, predicting
C
- Cage, Nicolas
- Canadian Automobile Association
- Canadian Tire
- car crashes and harm, predicting
- CareerBuilder
- Carlin, George
- Carlson, Gretchen
- Carnegie Mellon University
- cars, “orange lemons,”
- car services passenger destination, predicting
- CART decision trees
- causality
- cell phone industry
- consumer behavior and
- dropped calls, predicting
- GPS data and location predicting
- Telenor (Norway)
- CellTel (African telecom)
- Centers for Medicare and Medicaid Services
- Cerebellum Capital
- Chabbert, Martin
- Chaffee, Alexander Day
- CHAID
- Chaouchi, Mohamed
- Charlotte Rescue Mission
- Chase Bank
- actuarial approach used by
- churn modeling
- data, learning from
- economic recession and risk
- learning from data
- mergers and growth of
- microrisks faced by
- mortgage risk decision trees
- mortgage risks, predicting
- mortgage value estimation
- predictive models and success
- risks faced by
- Steinberg as leader
- cheating, predicting
- check fraud, predicting
- chess-playing computers
- Chicago Police Department
- Chopra, Sameer
- Chu-Carroll, Jennifer
- churn modeling
- Cialdini, Robert
- Citibank
- Citigroup
- Citizens Bank
- City of Boston
- City of Chicago
- City of New York
- city power, predicting fault in
- city regulations, crime predictions and
- civil liberties, risks to
- Clarke, Arthur C.
- clicks, predicting
- clinical trial recruitment, predicting
- Clinton, Hillary
- cloning customers
- Coase, Ronald
- Colbert, Stephen
- Colburn, Rafe
- collection agencies, PA for
- collective intelligence
- collective learning
- Columbia University
- Commander Data
- company networks, predicting fault in
- Competing on Analytics: The New Science of Winning (Davenport and Harris)
- competitions. See PA competitions
- computational linguistics. See also natural language processing (NLP)
- computer “intelligence.” See artificial intelligence (AI)
- computer programs, decision trees and
- computers
- creation of
- Deep Blue computer
- human brain versus
- viruses on, predicting
- See also artificial intelligence; machine learning; Watson computer Jeopardy! challenge
- computer science
- Con Edison
- conferences
- Congo massacres
- consumer behavior, insights on
- contests. See PA competitions
- Continental Airlines
- control sets
- corporate roll-ups
- couponing, predictive
- Coursera
- Cox Communications
- Craig, Roger
- credit cards
- fraud detection for
- payment systems, predicting fault in
- credit risk
- credit scores and
- typing and
- Crichton, Michael
- crime fighting and fraud detection
- cheating in board games
- credit card fraud
- in financial institutions
- fraud, defined
- fraudulent transactions
- IRS tax fraud
- machine injustice
- network intrusion
- PA application
- PA for
- prejudice in, risk of
- spam filtering
- crime prediction for law enforcement
- automatic suspect discovery (ASD) and NSA
- cybercrime
- ethical risks in
- fighting, PA for
- judicial decisions and
- murder, predicting
- PA application
- PA examples and insights
- prediction models for, pros and cons of
- recidivism
- Uber and behavior
- crowdsourcing
- collective intelligence and
- Kaggle PA crowdsourcing contests
- noncompetitive crowdsourcing
- PA and
- Cruise, Tom
- customer need, predicting fault in
- customer retention
- cancellations and predicting
- with churn modeling
- with churn uplift modeling
- contacting customers and
- feedback and dissatisfaction
- customization, perils of
- cybercrime
- cybernetics
D
- D’Arcy, Aoife
- data, sharing and using
- automatic suspect discovery (ASD) and NSA
- battle over mining
- choosing what to collect
- growth and rates of expansion
- policies for
- privacy concerns and
- data, types and sources of
- big data
- buzzwords for
- consumption and
- employee data
- fake data
- free public data
- learning data
- location data
- medical data
- personal data
- social media posts
- textual data
- data, value of
- about
- learning from data
- machine learning and
- personal data
- as “the new oil,”
- Dataclysm (Rudder)
- Data Effect,The
- data gluts
- Data.gov
- data hustlers and prospectors
- data mining
- Data Mining for Managers (Boire)
- Data Mining Techniques (Linoff, Berry)
- data preparation phase
- data science
- Data-sim (Lohr)
- Data Smart (Foreman)
- data storage
- dating websites
- attractiveness ratings and success
- consumer behavior on
- predicting on
- Davenport, Thomas H.
- death predictions
- “Deathwatch” (Siegel)
- deception, predicting
- Decision Management Systems (Taylor)
- decision trees
- CART decision trees
- circle single model
- decision boundaries in
- getting data from
- in machine learning
- methods competing with
- mortgage risk decision trees
- overlearning and assuming
- random forests
- uplift trees
- deduction versus induction
- Deep Blue computer
- DeepQA
- deliveries, predicting
- Delta Financial
- Deming, William Edwards
- Democratic National Committee (DNC)
- Deshpande, Bala
- Dey, Anindya
- Dhar, Vasant
- diapers and beer, behavior and
- Dick, Philip K.
- differential response modeling. See uplift modeling
- discrimination, risks of
- disease, predicting
- Disraeli, Benjamin
- divorce
- dolls and candy bars, behavior and
- Domingoes, Pedro
- donations and giving, predicting
- do-overs
- Dormehl, Luke
- downlift
- driver inattentiveness, predicting
- driverless cars
- Drucker, Peter
- drugs effects and use, predicting
- “Dry Bones” (song)
- DTE Energy
- Dubner, Stephen J.
- Duhigg, Charles
- dunnhumby
- Dyson, George
E
- Earthlink.com
- Echo Nest
- economic recession
- education
- grades, predicting
- guided studying for targeted learning
- PA for
- student dropout risk, predicting
- student knowledge and performance, predicting
- eHarmony
- Eindhoven University
- Eindhoven University, Netherlands
- Einstein, Albert
- Elder, John
- about
- “Are Orange Cars Really not Lemons?,”
- black-box trading systems
- on employee death predictions
- on generalization paradox
- in Netflix Prize competition
- on passion for science
- on power of data
- risks taken by
- on “vast search,”
- Elder Research, Inc.
- elections, crime rates and
- electoral politics
- Hillary for America 2016 Campaign
- musical taste political affiliation
- Obama for America 2012 Campaign
- Obama for America Campaign
- uplift modeling applications for
- voter persuasion, predicting
- electronic equipment, predicting fault in
- Elements of Statistical Learning, The (Hastie, Tibshirani, Friedman)
- Elie Tahari
- e-mail
- consumer behavior and addresses for
- government storage of
- Hotmail.com
- phishing e-mails
- spam filtering for
- emotions
- in blog posts
- human behavior and
- mood predictions and
- See also human behavior
- employee longevity, predicting
- employees and staff
- job applications and positions
- job performance, predicting
- job promotions and retention
- job skills, predicting
- LinkedIn for career predictions
- privacy concerns and data on
- quitting, predicting
- Energex (Australia)
- energy consumption, predicting
- Ensemble Effect, The
- Ensemble Experts
- ensemble models
- about
- automatic suspect discovery (ASD) and
- CART decision trees and bagging
- collective intelligence in
- complexity in
- crowdsourcing and
- generalization paradox and
- IBM Watson question answering computer and
- IRS (tax fraud)
- meta-learning and
- Nature Conservancy (donations)
- Netflix (movie recommendations)
- Nokia-Siemens Networks (dropped calls)
- University of California, Berkeley (brain activity)
- for uplift modeling
- U.S. Department of Defense (fraudulent invoices)
- U.S. Department of Defense Service (fraudulent invoices)
- U.S. Special Forces (job performance)
- Ensemble team
- Epagogix
- erectile dysfunction
- Experian
- Exxon Mobil Corp.
F
- “Fab Four” inventors
- Facebook
- data glut on
- data on
- fake data on
- friendships, predicting
- happiness as contagious on
- job performance and profiles on
- social effect of
- student performance PA contest
- facial recognition
- Failure of Risk Management, The (Hubbard)
- false conclusions, avoiding
- false positives (false alarms)
- family and personal life, PA for
- Farrell, Colin
- fault detection for safety and efficiency, PA for
- Federal Trade Commission
- FedEx
- Femto-photography
- Ferguson, Andrew
- Ferrucci, David
- FICO
- Fidelity Investments
- finance and accounting, fraud detection in
- finance websites, behavior on
- financial risk and insurance, PA for
- Fingerhut
- Finland
- fire, predicting
- First Tennessee Bank
- Fisher, Ronald
- Fleming, Alexander
- flight delays, predicting fault in
- Flight Risks, predicting
- Flirtback computer program
- Florida Department of Juvenile Justice
- fMRI brain scans
- Foldit
- Food and Drug Administration (FDA)
- Fooled by Randomness (Taleb)
- Ford Motor Co.
- forecasting
- Foreman, John W.
- Formula, The (Dormehl)
- Fox & Friends (TV show)
- Franklin, Benjamin
- Franks, Bill
- fraud, defined
- fraud detection. See also crime fighting and fraud detection
- Freakonomics Radio
- Freakonomics Radio
- frequency
- Friedman, Jerome
- friendships, predicting
- Fukuman, Audrey
- Fulcher, Christopher
- Fundamentals of Machine Learning for Predictive Data Analytics (Kelleher, MacNamee, D’Arcy)
- fund-raising, predicting in
- Furnas, Alexander
- future, views on
- human nature and knowing about
- predictions for 2022
- uncertainty of
G
- Galileo
- Gates, Bill
- generalization paradox
- Ghani, Rayid
- Gilbert, Allen
- Gladwell, Malcolm
- GlaxoSmithKline (U.K.)
- Goethe, Johann Wolfgang von
- Goldbloom, Anthony
- Gondek, David
- Google
- mouse clicks, measuring for predictions
- privacy policies
- Schmidt
- searches for playing Jeopardy!
- self-driving cars
- spam filtering
- Google Adwords
- Google Flu Trends
- Google Page Rank
- government
- data storage by
- fraud detection for invoices
- PA for
- public access to data
- See also individual names of U.S. government agencies
- GPS data
- grades, predicting
- grant awards, predicting
- Greenwald, Glenn
- Grockit
- Groundhog Day (film)
- Grundhoefer, Michael
H
- hackers, predicting
- HAL (intelligent computer)
- Halder, Gitali
- Handbook of Statistical Analysis and Data Mining Applications (Nisbet, Elder, Miner)
- Hansell, Saul
- happiness, social effect and
- Harbor Sweets
- Harcourt, Bernard
- Harrah's Las Vegas
- Harris, Jeanne
- Harvard Medical School
- Harvard University
- Hastings, Reed
- healthcare
- death predictions in
- health risks, predicting
- hospital admissions, predicting
- influenza, predicting
- insurance companies, predicting
- medical research, predicting in
- medical treatments, risks for wrong predictions in
- medical treatments, testing persuasion in
- PA for
- personalized medicine, uplift modeling applications for
- health insurance companies, PA for
- Hebrew University
- Heisenberg, Werner Karl
- Helle, Eva
- Helsinki Brain Research Centre
- Hennessey, Kathleen
- Heraclitus
- Heritage Health Prize
- Heritage Provider Network
- Hewlett Foundation
- Hewlett-Packard (HP)
- employee data used by
- financial savings and benefits of PA
- Global Business Services (GBS)
- quitting and Flight Risks, predicting
- sales leads, predicting
- turnover rates at
- warranty claims and fraud detection
- Hillary for America 2016 Campaign
- HIV progression, predicting
- HIV treatments, uplift modeling for
- Hollifield, Stephen
- Holmes, Sherlock
- Hopper
- hormone replacement, coronary disease and
- hospital admissions, predicting
- Hotmail.com
- House (TV show)
- “How Companies Learn Your Secrets” (Duhigg)
- Howe, Jeff
- HP. See Hewlett-Packard (HP)
- Hubbard, Douglas
- human behavior
- collective intelligence
- consumer behavior insights
- emotions and mood prediction
- mistakes, predicting
- social effect and
- human genome
- human language
- natural language processing (NLP)
- PA for
- persuasion and influence in
- human resources. See employees and staff
I
- IBM
- corporate roll-ups
- Deep Blue computer
- DeepQA project
- Iambic IBM AI
- mind-reading technology
- natural language processing research
- sales leads, predicting
- student performance PA contest
- T. J. Watson Research Center
- value of
- See also Watson computer Jeopardy! challenge
- ID3
- impact modeling. See uplift modeling
- Imperium
- inappropriate comments, predicting
- incremental impact modeling. See uplift modeling
- incremental response modeling. See uplift modeling
- India
- Indiana University
- Induction Effect, The
- induction versus deduction
- inductive bias
- ineffective advertising, predicting
- infidelity, predicting
- Infinity Insurance
- influence. See persuasion and influence
- influenza, predicting
- information technology (IT) systems, predicting fault in
- InnoCentive
- insults, predicting
- insurance claims
- automobile insurance fraud, predicting
- death predictions and
- financial risk predicting in
- health insurance
- life insurance companies
- life nsurance companies
- Integral Solutions Limited
- Internal Revenue Service (IRS)
- International Conference on Very Large Databases
- Iowa State University
- iPhone. See Apple Siri
- Iran
- Israel Institute of Technology
J
- Japan
- Jennings, Ken
- Jeopardy!(TV show). See Watson computer Jeopardy! challenge
- Jevons, William Stanley
- Jewell, Robert
- jobs and employment. See employees and staff
- Jones, Chris
- Journal of Computational Science
- JPMorgan Chase. See Chase Bank
- judicial decisions, crime prediction and
- Jung, Tommy
- Jurassic Park (Crichton)
- Just Giving
K
- Kaggle
- Kane, Katherine
- Kasparov, Garry
- KDnuggets
- Keane, Bil
- “keep it simple, stupid” (KISS) principle
- Kelleher, John D.
- Khabaza, Tom
- killing, predicting
- King, Eric
- Kiva
- Kmart
- knee surgery choices
- knowledge (for education),predicting
- Kotu, Vijay
- Kretsinger, Stein
- Kroger
- Kuneva, Meglena
- Kurtz, Ellen
- Kurzweil, Ray
L
- language. See human language
- Lashkar-e-Taiba
- law enforcement. See crime prediction for law enforcement
- lead poisoning from paint, predicting
- learning
- about
- collective learning
- education—guided studying for targeted learning
- learning from data
- memorization versus
- overlearning, avoiding
- Leinweber, David
- Leno, Jay
- Levant, Oscar
- Levitt, Stephen
- Lewis, Michael
- lies, predicting. See deception, predicting
- Lie to Me (TV show)
- life insurance companies, PA for
- Life Line Screening
- lift
- Lindbergh, Charles
- LinkedIn
- friendships, predicting
- job skills, predicting
- Linoff, Gordon S.
- Linux operating systems
- Lloyds TSB
- loan default risks, predicting
- location data
- logistic regression
- Lohr, Steve
- London Stock Exchange
- Los Angeles Police Department
- Lotti, Michael
- Loukides, Mike
- love, predicting
- Lynyrd Skynyrd (band)
M
- MacDowell, Andie
- machine learning
- about
- courses on
- in crime prediction
- data preparation phase for
- decision trees in
- induction and
- induction versus deduction
- learning data
- learning from mistakes in
- learning machines, building
- overlearning
- predictive models, building with
- silence, concept of
- testing and validating data
- univariate versus multivariate models
- See also Watson computer Jeopardy! challenge
- machine risk
- MacNamee, Brian
- macroscopic risks
- Mac versus Windows users
- Madrigal, Alexis
- Magic 8 Ball toy
- Mao, Huina
- maritime incidents, predicting
- marketing and advertising
- banner ads and consumer behavior
- mouse clicks and consumer behavior
- targeting direct marketing
- marketing models
- do-overs in
- messages, creative design for
- Persuasion Effect, The
- quantum humans, influencing
- response uplift modeling
- marketing segmentation, decision trees and
- marriage and divorce, predicting
- Mars Climate Orbiter
- Martin, Andres D.
- Maryland, crime predictions in
- Massachusetts Institute of Technology (MIT)
- Master Algorithm, The (Domingos)
- Match.com
- Matrix, The (film)
- McCord, Michael
- McKinsey reports
- McNamara, Robert
- Mechanical Turk
- medical claims, fraudulent
- medical treatments. See healthcare
- memorization versus learning
- Memphis (TN) Police Department
- metadata
- meta-learning
- Mexican Tax Administration
- Miami Police Department
- Michelangelo
- microloan defaults, predicting
- Microsoft
- Milne, A. A.
- Mimoni (Mexico)
- mind-reading technology
- Miner, Gary
- Minority Report (film)
- Missouri
- Mitchell, Tom
- mobile operators. See cell phone industry
- moneyballing, concept of
- mood labels
- mood prediction, blogs and
- mortgage prepays and risk, predicting
- mortgage risk decision trees
- mortgage value estimation
- mouse clicks, predicting
- movie hits, predicting
- movie recommendations
- movies
- MTV
- MultiCare Health System (Washington State)
- “multiple comparisons problem”/multiple comparisons trap”
- multivariate models
- murder, predicting
- Murray, Bill
- music, stroke recovery and
- musical taste, political affliation and
- Muslims
N
- Naïve Bayes
- Naked Future, The (Tucker)
- NASA
- Apollo 11
- Mars Climate Orbiter
- PA contests sponsored by
- on space exploration
- National Insurance Crime Bureau
- National Security Agency (NSA)
- National Transportation Safety Board
- natural language processing (NLP)
- Nature Conservancy
- Nazarko, Edward
- Nerds on Wall Street (Leinweber)
- Netflix movie recommendations
- Netflix Prize
- about
- competition and winning
- crowdsourcing and PA for
- meta-learning and ensemble models in
- PragmaticTheory team
- Netherlands
- net lift modeling. See uplift modeling
- net response modeling. See uplift modeling
- net weight of evidence (NWOE)
- network intrusion detection
- New South Wales, Australia
- New York City Medicaid
- New York State
- New York Times, The
- Next (Dick)
- Ng, Andrew
- Nightcrawler (superhero)
- Nineteen Eighty-Four (Orwell)
- 99designs
- Nisbet, Robert
- “no free lunch” theorem
- Nokia
- Nokia-Siemens Networks
- nonprofit organizations, PA for
- Noonan, Peggy
- No Place to Hide (Greenwald)
- Northwestern University Kellogg School of Management
- nuclear reactors, predicting fault in
- null hypothesis
- Numerati, The (Baker)
O
- Obama for America 2012 Campaign
- observation, power of
- Occam's razor
- O’Connor, Sandra Day
- office equipment, predicting fault in
- Oi (Brasil Telecom)
- oil flow rates, predicting
- oil refinery safety incidents, predicting
- OkCupid
- Oklahoma State University
- O’Leary, Martin
- Olshen, Richard
- 1–800-FLOWERS
- 1-sided equality of proportions hypothesis test
- Online Privacy Foundation
- Oogway
- open data movement
- open question answering
- open source software
- Optus (Australia)
- “orange lemons” (cars)
- Orbitz
- Oregon, crime prediction in
- organizational learning
- organizational risk management
- Orwell, George
- Osco Drug
- overfitting. See overlearning
- overlearning
- Oz, Mehmet
P
- PA (predictive analytics)
- about
- assumption about NSA's use of
- choosing what to predict
- in crime fighting and fraud detection
- crowdsourcing and
- defined
- in family and personal life
- in fault detection for safety and efficiency
- in finance and accounting fraud detection
- in financial risk and insurance
- forecasting versus
- frequently asked questions about
- in government, politics, nonprofit, and education
- in healthcare
- in human language understanding, thought, and psychology
- launching and taking action with PA
- in law enforcement and fraud detection
- in marketing, advertising, and the Web
- “orange lemons” and
- overview
- risk-oriented definition of
- text analytics
- in workforce (staff and employees)
- PA (predictive analytics) applications
- black-box trading
- blog entry anxiety detection
- board games, playing
- credit risk
- crime prediction
- customer retention with churn modeling
- customer retention with churn uplift modeling
- defined by
- education—guided studying for targeted learning
- employee retention
- fraud detection
- mortgage value estimation
- movie recommendations
- network intrusion detection
- open question answering
- political campaigning with voter persuasion modeling
- predictive advertisement targeting
- pregnancy prediction
- recidivism prediction for law enforcement
- spam filtering
- targeting direct marketing
- uplift modeling applications
- See also Central Tables insert
- PA (predictive analytics) competitions and contests
- in astronomy and science
- for design and games
- for educational applications
- Kaggle crowdsourcing contests
- Netflix Prize
- PA (predictive analytics) insights
- consumer behavior
- crime and law enforcement
- finance and insurance
- miscellaneous
- Palmisano, Sam
- Panchoo, Gary
- Pandora
- parole and sentencing, predicting
- Parsons, Christi
- PAW (Predictive Analytics World) conferences
- payment processors, predicting fault in
- PayPal
- penicillin
- Pennsylvania
- personalization, perils of
- persuasion and influence
- observation and
- persuadable individuals, identifying
- predicting
- scientifically proving persuasion
- testing in business
- uplift modeling for
- voter persuasion modeling
- Persuasion Effect, The
- persuasion modeling. See uplift modeling
- Petrified Forest National Park, Arizona
- Pfizer
- Philadelphia (PA) Police Department
- photographs
- caption quality and likability
- growth of in data glut
- Piotte, Martin
- Pitney Bowes
- Pittsburgh Science of Learning
- Pole, Andrew
- police departments. See crime prediction for law enforcement
- politics, PA for. See also electoral politics
- Porter, Daniel
- Portrait Software
- Post hoc, ergo propter hoc
- Power of Habit: Why We Do What We Do in Life and Business (Duhigg)
- PragmaticTheory team
- prediction
- benefits of
- choosing what to predict
- collective obsession with
- future predictions
- good versus bad
- limits of
- organizational learning and
- prediction, effects of and on
- about
- Data Effect,The
- Ensemble Effect, The
- Induction Effect, The
- Persuasion Effect, The
- Prediction Effect, The
- prediction markets
- predictive analytics. See PA (predictive analytics)
- Predictive Analytics (Siegel), website of
- Predictive Analytics and Data Mining (Kotu, Deshpande)
- Predictive Analytics Applied (training program)
- Predictive Analytics for Dummies (Bari, Chaouchi, Jung)
- Predictive Analytics Guide
- Predictive Analytics Times
- Predictive Analytics World (PAW)
- Predictive Analytics World (training programs)
- Predictive Analytics World (PAW) conferences
- Predictive Marketing and Analytics (Strickland)
- predictive models
- about
- action and decision making
- causality and
- defined
- deployment phase
- Elder's success in
- going live
- machine learning and building
- marketing models
- observation and
- overlearning and assuming
- personalization and
- response modeling
- response uplift modeling
- risks in
- univariate versus multivariate
- uplift modeling
- See also ensemble models
- PredictiveNotes.com
- predictive technology. See also machine learning
- predictor variables
- pregnancy and birth, predicting
- customer pregnancy and buying behavior
- premature births
- prejudice, risk of
- PREMIER Bankcard
- prescriptive analytics
- privacy
- Google policies on
- insight versus intrusion regarding
- predicted consumer data and
- probability, The Data Effect and
- profiling customers
- Progressive Insurance
- Psych (TV show)
- psychology
- predictive analysis in
- schizophrenia, predicting
- psychopathy, predicting
- purchases, predicting
- p-value
Q
- Quadstone
R
- Radcliffe, Nicholas
- Radica Games
- Ralph's
- random forests
- Rebellion Research
- recency
- recidivism prediction for law enforcement
- recommendation systems
- Reed Elsevier
- reliability modeling
- REO Speedwagon (band)
- response modeling
- drawbacks of
- examples of
- targeted marketing with
- response rates
- response uplift modeling
- restaurant health code violations, predicting
- retail websites, behavior on
- retirement, health and
- Richmond (VA) Police Department
- RightShip
- Rio Salado Community College
- risk management
- Riskprediction.org.uk
- risk scores
- Risky Business (film)
- Romney, Mitt
- Rousseff, Dilma
- Royal Astronomy Society
- R software
- Rudder, Christian
- Russell, Bertrand
- Rutter, Brad
S
- safety and efficiency, PA for
- Safeway
- sales leads, predicting
- Salford Systems
- “Sameer Chopra's Hotlist of Training Resources for Predictive Analytics” (Predictive Analytics Times)
- Sanders, Bernie
- Santa Cruz (CA) Police Department
- sarcasm, in review
- Sartre, Jean-Paul
- SAS
- satellites, predicting fault in
- satisficing
- Schamberg, Lisa
- Scheer, Robert
- schizophrenia, predicting
- Schlitz, Don
- Schmidt, Eric
- Schwartz, Ari
- Science magazine
- scientific discovery, automating
- Seattle Times
- security levels, predicting
- self-driving cars
- Selfridge, Oliver
- Semisonic (band)
- sepsis, predicting
- Sesame Street
- Sesenbrenner, James
- Sessions, Roger
- Shakespeare, William
- Shaw, George Bernard
- Shearer, Colin
- Shell
- shopping habits, predicting
- sickness, predicting
- Siegel, Eric
- silence, concept of
- Silver, Nate
- Simpsons, The (TV show)
- Siri
- Sisters of Mercy Health Systems
- smoking and smokers
- health problems and causation for
- motion disorders and
- social effect and quitting
- Snowden, Edward
- Sobel, David
- social effect
- social media networks
- data glut on
- happiness as contagious on healthcare
- PA for
- spam filtering on
- YouTube
- See also Facebook
- sociology, uplift modeling applications for
- SpaceShipOne
- spam, predicting
- spam filtering
- Spider-Man (film)
- sporting events, crime rates and
- sports cars
- Spotify
- Sprint
- SPSS
- staff behavior. See employees and staff
- Standard & Poor's (S&P) 500
- Stanford University
- staplers, hiring behavior and
- Star Trek (TV shows and films)
- statistics
- StatSoft
- stealing, predicting
- Steinberg, Dan
- stock market predictions
- black-box trading systems
- Standard & Poor's (S&P) 500
- Stone, Charles
- Stop & Shop
- street crime, predicting
- Strickland, Jeffrey
- student dropout risks, predicting
- student performance, predicting
- suicide bombers, life insurance and
- Sun Microsystems
- Super Crunchers (Ayres)
- SuperFreakonomics (Levitt and Dubner)
- supermarket visits, predicting
- surgical site infections, predicting
- Surowiecki, James
- Sweden
- system failures
- Szarkowski, John
T
- Taleb, Nassim Nicholas
- Talking Heads (band)
- Target
- baby registry at
- couponing predictively
- customer pregnancy predictions
- privacy concerns in PA
- product choices and personalized recommendations
- purchases and target marketing predictions
- targeted marketing with response uplift modeling
- targeting direct marketing
- target shuffling
- taxonomies
- tax refunds
- Taylor, James
- TCP/IP
- Telenor (Norway)
- Teragram
- terrorism, predicting
- Tesco (U.K.)
- test data
- test preparation, predicting
- text analytics
- Text Analytics World
- textbooks
- text data
- text mining. See text analytics
- They Know Everything about You (Scheer)
- thought and understanding, PA for
- thoughtcrimes
- Tibshirani, Robert
- Titantic (ship)
- T. J. Watson Research Center
- tobacco. See smoking and smokers
- Tolstoy, Leo
- traffic, predicting
- training data. See also learning
- training programs
- train tracks, predicting fault in
- Trammps (band)
- travel websites, behavior on
- Trebek, Alex
- TREC QA (Text Retrieval Conference—Question Answering)
- truck driver fatigue, predicting
- true lift modeling. See uplift modeling
- true response modeling. See uplift modeling
- TTX
- Tucker, Patrick
- Tumblr
- Turing, Alan and the Turing test
- Twenty Questions game, decision trees and
- Twilight Zone (TV show)
- Twitter
- 2001: A Space Odyssey (film)
- data glut on
- fake accounts on
- mood prediction research via
- person-to-person interactions saved by
- 2degrees (New Zealand)
- typing, credit risk and
U
- Uber
- uncertainty principle
- understanding and thought, predictive analysis in
- univariate models
- University of Alabama
- University of Buffalo
- University of California, Berkeley
- University of Colorado
- University of Helsinki
- University of Iowa Hospitals & Clinics
- University of Massachusetts
- University of Melbourne
- University of New Mexico
- University of Phoenix
- University of Pittsburgh Medical Center
- University of Southern California
- University of Texas
- University of the District of Columbia
- University of Utah
- University of Zurich
- uplift modeling
- customer retention with churn uplift modeling
- downlift in
- influence across industries
- mechanics and workings of
- Obama for America Campaign and
- The Persuasion Effect, and
- response uplift modeling
- targeted marketing with response uplift modeling
- Telenor using
- U.S. Bank using
- uplift trees
- UPS
- U.S. Armed Forces
- U.S. Bank
- U.S. Department of Defense
- U.S. Department of Defense Finance and Accounting Service
- U.S. Food and Drug Administration (FDA)
- U.S. government. See government
- U.S. National Institute of Justice
- U.S. National Security Agency
- U.S. Naval Special Warfare Command
- U.S. Postal Service
- U.S. Social Security Administration
- U.S. Special Forces
- U.S. Supreme Court
- Utah Data Center
V
- variables. See predictor variables
- “vast search,”
- Vermont Country Store
- Vineland (NJ) Police Department
- viral tweets/posts, predicting
- Virginia, crime prediction in
- Volinsky, Chris
- voter persuasion, predicting
W
- Wagner, Daniel
- Walgreens
- Wall Street Journal, The
- Walmart
- Wanamaker, John
- warranty claim fraud, predicting
- washing machines, fault detection in
- Watson, Thomas J.
- Watson computer Jeopardy! challenge
- about
- artificial intelligence (AI) and
- candidate answer evidence routines
- confidence, estimation of
- Craig's question predictions for
- creation and programming of
- ensemble models and evidence
- Jeopardy! questions as data for
- language processing and machine learning
- language processing for answering questions
- moneyballing Jeopardy!
- natural language processing (NLP) and
- open question answering
- playing and winning
- praise and success of
- predictive models and predicting answers
- predictive models for predicting answers
- predictive models for question answering
- Siri versus Watson
- speed in answering for
- Web browsing, behavior and
- Webster, Eric
- Wells Fargo
- Whiting, Rick
- Who Wants to Be a Millionaire? (TV show)
- “wider” data
- Wiener, Norbert
- Wikipedia
- editor attrition predicting
- entries as data
- noncompetitive crowdsourcing in
- Wilde, Oscar
- Wilson, Earl
- Windows versus Mac users
- Winn-Dixie
- Wired magazine
- Wisdom of Crowds, The (Surowiecki)
- WolframAlpha
- WordPress
- workplace injuries, predicting
- Wright, Andy
- Wright brothers
- Wriston, Walter
X
- X Prize
Y
- Yahoo!
- Yahoo! Labs
- Yes! 50 Scientifically Proven Ways to Be Persuasive (Cialdini et al.)
- yoga, mood and
- YouTube
Z
- Zeng, Xiao-Jun
- Zhou, Jay
The Notes (www.PredictiveNotes.com)—120 pages of citations and comments pertaining to the chapters—available online only.
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