Predicting the Unpredictable

2016-11-08

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Author: Susan Elizabeth Hough
Publisher: Princeton University Press
ISBN: 0691173303
Category : Science
Languages : en
Pages : 276

An earthquake can strike without warning and wreak horrific destruction and death, whether it's the catastrophic 2010 quake that took a devastating toll on the island nation of Haiti or a future great earthquake on the San Andreas Fault in California, which scientists know is inevitable. Yet despite rapid advances in earthquake science, seismologists still can’t predict when the Big One will hit. Predicting the Unpredictable explains why, exploring the fact and fiction behind the science—and pseudoscience—of earthquake prediction. Susan Hough traces the continuing quest by seismologists to forecast the time, location, and magnitude of future quakes. She brings readers into the laboratory and out into the field—describing attempts that have raised hopes only to collapse under scrutiny, as well as approaches that seem to hold future promise. She also ventures to the fringes of pseudoscience to consider ideas outside the scientific mainstream. An entertaining and accessible foray into the world of earthquake prediction, Predicting the Unpredictable illuminates the unique challenges of predicting earthquakes.

Predicting Success

2014-09-22

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Author: David Lahey
Publisher: John Wiley & Sons
ISBN: 1118985990
Category : Business & Economics
Languages : en
Pages : 192

Make the right hires every time, with an analytical approach totalent Predicting Success is a practical guide to finding theperfect member for your team. By applying the principles and toolsof human analytics to the workplace, you'll avoid bad culture fits,mismatched skillsets, entitled workers, and other hiring misstepsthat drain the team of productivity and morale. This book providesguidance toward implementing tools like the Predictive Index®,behavior analytics, hiring assessments, and other practicalresources to build your best team and achieve the best outcomes.Written by a human analytics specialist who applies theseprinciples daily, this book is the manager's guide to aligningpeople with business strategy to find the exact person your team ismissing. An avalanche of research describes an evolving businesslandscape that will soon be populated by workers in jobs that don'tfit. This is bad news for both the workers and the companies, asbad hires affect outcomes on the individual and organizationallevel, and can potentially hinder progress long after the situationhas been rectified. Predicting Success is a guide toavoiding that by integrating analytical tools into the hiringprocess from the start. Hire without the worry of mismatched expectations Apply practical analytics tools to the hiring process Build the right team and avoid disconnected or dissatisfiedworkers Stop seeing candidates as "chances," and start seeing them asopportunities Analytics has proved to be integral in the finance, tech,marketing, and banking industries, but when applied to talentacquisition, it can build the team that takes the company to thenext level. If the future will be full of unhappy workers inunderperforming companies, getting out from under that weight aheadof time would confer a major advantage. Predicting Successprovides evidence-based strategies that help you find precisely thetalent you need.

Predictive Analytics

2016-01-13

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Author: Eric Siegel
Publisher: John Wiley & Sons
ISBN: 1119145686
Category : Business & Economics
Languages : en
Pages : 368

"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Applied Predictive Modeling

2013-05-17

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Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 600

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Predicting Personality

2019-11-12

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Author: Drew D'Agostino
Publisher: John Wiley & Sons
ISBN: 1119630967
Category : Business & Economics
Languages : en
Pages : 249

The ultimate playbook for using artificial intelligence to communicate effectively, build teams, and win customers Not long ago, we imagined a hyper-connected world full of trust and openness—a world where effortless communication would bring about a new understanding between people everywhere. Judging from our current environment, this vision of the future may have been overly optimistic. With infinite channels and countless voices flooding them with messages, most people have become highly skeptical and guarded by necessity. As a result, communication is much harder than ever before. Despite the unprecedented connectivity enabled by modern technology, we are far less likely to trust and to invest the time needed to build strong relationships. How can we use technology to reverse this trend? A groundbreaking new branch of artificial intelligence—Personality AI—may be the answer. Combining traditional machine learning, data analytics, and behavioral psychology, Personality AI helps professional communicators tear down walls, establish trust with their audiences, and utilize data to build meaningful relationships, strengthen empathy, and win more customers. Predicting Personality is a practical, real-world playbook for any individual or business whose success hinges on the ability to communicate effectively and build teams. Authors Drew D’Agostino and Greg Skloot—CEO and President, respectively, of Crystal, the app that tells you anyone's personality—show you how businesses can leverage Personality AI and machine learning to grow faster and communicate more effectively than was previously possible. This reader-friendly guide teaches you what Personality AI is, how it works, and demonstrates its practical applications in both life and business. This book: ● Explains how to understand personality types in various contexts, including sales, recruiting, coaching ● Provides guidelines for using personality data to learn and execute ● Explores ethics and compliance considerations surrounding the use of Personality AI ● Offers valuable insights from a leader in the business applications of Personality AI Predicting Personality: Using AI to Understand People and Win More Business is a must-have guide for C-suite executives, sales and marketing professionals, coaches, recruiters, and business owners.

Predicting the Future

1998-01-01

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Author: Nicholas Rescher
Publisher: SUNY Press
ISBN: 9780791435533
Category : Social Science
Languages : en
Pages : 334

The future obviously matters to us. It is, after all, where we'll be spending the rest of our lives. We need some degree of foresight if we are to make effective plans for managing our affairs. Much that we would like to know in advance cannot be predicted. But a vast amount of successful prediction is nonetheless possible, especially in the context of applied sciences such as medicine, meteorology, and engineering. This book examines our prospects for finding out about the future in advance. It addresses questions such as why prediction is possible in some areas and not others; what sorts of methods and resources make successful prediction possible; and what obstacles limit the predictive venture. Nicholas Rescher develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its promises and problems. Predicting the Future considers the anthropological and historical background of the predictive enterprise. It also examines the conceptual, epistemic, and ontological principles that set the stage for predictive efforts. In short, Rescher explores the basic features of the predictive situation and considers their broader implications in science, in philosophy, and in the management of our daily affairs.

Fundamentals of Clinical Data Science

2018-12-21

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Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Prediction, Learning, and Games

2006-03-13

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Author: Nicolo Cesa-Bianchi
Publisher: Cambridge University Press
ISBN: 113945482X
Category : Computers
Languages : en
Pages : 4

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.

Predicting the Future

2013-06-12

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Author: Henry Abarbanel
Publisher: Springer
ISBN: 1461472180
Category : Science
Languages : en
Pages : 253

Through the development of an exact path integral for use in transferring information from observations to a model of the observed system, the author provides a general framework for the discussion of model building and evaluation across disciplines. Through many illustrative examples drawn from models in neuroscience, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model’s consistency with observations is explored.

Clinical Prediction Models

2019-07-22

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Author: Ewout W. Steyerberg
Publisher: Springer
ISBN: 3030163997
Category : Medical
Languages : en
Pages : 558

The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies