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How to Automate Machine Learning Model Tuning

Trent Bradberry

December 7, 2018

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I’ve long heard that “a watched pot never boils,” but when I am heavily invested in the outcome of a process, I still tend to monitor it intently. In my impatience I begin to wonder if my attention is worse than useless, and is actually impeding the progress of the process! When considering machine learning models, it may be true. Let me explain.

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Top 3 Lessons Learned While Drinking from the Data Science Firehose

Sam Ballerini

November 30, 2018

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I had one hard requirement during my job search: wherever I ended up, I wanted to drink from the "data science firehose." I wanted to work alongside seasoned data scientists with diverse skillsets and an unadulterated passion for solving problems with data. I wanted to leave the office after my first day asking myself, “How in the world am I going to keep up with these people?” And that's exactly what I've gotten at Elder Research.

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Monte Carlo Simulation - a Venerable History

Peter Bruce

November 16, 2018

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One of the most consequential and valuable analytical tools in business is simulation, which helps us make decisions in the face of uncertainty.

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Simple, Attractive, and Wrong: An Introduction to Linearity Bias

Will Goodrum, Ph.D.

November 2, 2018

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This blog is 3rd in a series of short posts where we explore common biases that can impair analytics projects.

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Top 3 Objectives Before Starting an Analytics Project

Gerhard Pilcher

October 26, 2018

BLOG_Top 3 Objectives Before Starting an Analytics ProjectUnderstanding the organization’s business objectives and requirements, converting this knowledge into a definition of a problem, and developing a preliminary plan to solve that problem is crucial to the successful application of analytics and machine learning. In order to construct a successful model, the data scientist must understand how the business functions and how it will use the data. Even the most technologically advanced analytics model will produce trivial and possibly misleading results if it is disconnected from the purposes and goals of the business.

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Machine Learning: It is a Mistake to Lack Relevant Data

John Elder, Ph.D.

October 19, 2018

BLOG_It's a Mistake to Lack Relevant DataIn his Top 10 Data Science Mistakes Dr. John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #0 you will learn that the less probable the interesting event is, the more data it takes to obtain enough to generalize a model to unseen cases, and why some projects probably should not proceed until enough critical data is gathered to make them worthwhile.

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Finding Fraud When No Cases are Known

Carlos Blancarte

October 12, 2018

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Fraud detection is about finding needles in haystacks and requires reliably labeled instances of fraudulent (needle) and non-fraudulent (straw) behavior. A predictive model can be trained using these labels to learn the underlying patterns in the input variables that best separate fraud from non-fraud cases, and thereby estimate the fraud-likeness of any future case. Typically, the interesting cases are very scarce, in which case we might have to carefully up-sample the rare class and/or down-sample the abundant class to help the model pay enough attention to the rare class to be useful. But what do we do when labels are not just rare, but are completely absent?

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Hiring a Data Analytics Consultant

Jeff Deal

October 5, 2018

BLOG_Hiring Data Analytics Consultants-1In the earliest days of data analytics, our new clients would typically say, “Solve this problem for us.” As they saw the enduring power of analytics, their request then became, “We want to launch our own data analytics capability. Will you help us set it up?” However, growing an analytics capability from scratch is a huge challenge, and today more companies appreciate its difficulty. 

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Who Invented the Null Hypothesis?

Peter Bruce

September 28, 2018

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For many students, statistics is a troublesome subject, and the root of that trouble can be traced to the concept of the null hypothesis. In these days of big data, machine learning, and predictive analytics, formal hypothesis testing has receded in relative importance. Nonetheless, it retains considerable inertia and ability to cause difficulty - even in data science circles.

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Workshops, Workshops, Workshops!

Paul Derstine

September 14, 2018

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Predictive Analytics World for Government, the premier analytics and AI conference for government, starts next week. Keynote speakers include David Williams, USPS Board of Governors, Tom Davenport, a world-renowned analytics thought leader, author, and industry expert, and Dr. John Elder, Chairman and Founder of Elder Research, author and analytics practitioner. Elder Research will teach three workshops at the conference, offering insight on Machine Learning Methods, Deadly Analytics Mistakes, and Data Science for Managers.

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Fluency in The Language of Data Models

Michael Lieberman

September 7, 2018

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My job as a data scientist and research strategist is getting easier. Over the past 50 years, statisticians have developed a number of practical models that are highly effective to explain consumer patterns and predict consumer behavior. As new forms of computing power and information technology provide every increasing descriptions of individual-level purchasing tendencies, these models offer great value for business managers.

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Data Science, Statistics, and the "Method of Moments"

Peter Bruce

August 31, 2018


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I got my introduction to statistics via resampling, working with Julian Simon, an early resampling pioneer. Demonstrating this "brute force" computer method to my father, I saw that he was vaguely offended by its inelegance.

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Are We Using Machine Learning?

Gerhard Pilcher

August 24, 2018

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In the midst of a recent engagement an executive suddenly asked, “Are we using Machine Learning?”. This caught us off-guard; working in the field for many years, we use the “learning sciences” virtually every day to solve hard problems. Machine Learning (ML), Data Science (DS) and Artificial Intelligence (AI) are exciting and very powerful; still, we’re happy to use conventional techniques whenever they’re the best choice to solve the client’s challenge. 

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Using Machine Learning to Predict Parkinson’s Disease

Jennifer Schaff, PhD

August 17, 2018

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Recent research supported by the Michael J. Fox Foundation (MJFF) (and other benefactors) collected multifaceted data sets from patients with Parkinson’s Disease. They wanted to determine which medical test, or combination of tests, best predicts Parkinson’s disease.

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It is a Mistake to Ask the Wrong Questions

John Elder, Ph.D.

August 10, 2018

BLOG_It is a Mistake to Ask the Wrong QuestionsIn his Top 10 Data Science Mistakes Dr. John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #3 you will learn why it is very important to have the right project goal; that is, to aim at the right target; and even with the right project goal it is essential to also have an appropriate model goal.

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Automating Demand Forecasting with Machine Learning

Will Goodrum, Ph.D.

August 3, 2018

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Elder Research implemented an automated framework for time-series forecasting at a major logistics company. Our system, combining R and Apache Spark™, produces 35 million forecasts in under one hour, and selects the optimal time-series forecast algorithm in each of three forecasting windows. Forecast results from our framework were 88% accurate at a four-week horizon.

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3 Myths About the Normal Distribution

Peter Bruce

July 27, 2018

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Is the Normal Curve Normal?

I saw an article recently that referred to the normal curve as the data scientist's best friend, and it is certainly true that the normal distribution is ubiquitous in classical statistical theory. Still, it's overrated.

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Hype or Reality: The ROI of Machine Learning

Paul Derstine

July 20, 2018

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The hype around “the thinking sciences” — Artificial Intelligence, Machine Learning, and Data Science — is enormous, so it’s tempting to be skeptical of the return on investment (ROI) claimed. Still, most of the results are real. The capabilities of Data Science and Machine Learning, where models are inductively built from real history, have been growing steadily.

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Do Algorithms Have Bias?

Peter Bruce

July 6, 2018

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Algorithmic bias is a popular topic; see for example this article describing how Microsoft is working on a dashboard product to detect unfair bias in algorithms. When a typical person (not a statistician) uses the term "bias" they usually have in mind unfair prejudgment, or stacking of the deck, against a person based on some aspect of that person's identity (race, gender, ethnic background, religion, nationality, etc.). Until recently, "bias" meant something very different to statisticians.

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Sophisticated Text Analysis Is Hard, but it Works

John Elder, Ph.D.

June 29, 2018

BLOG_Sophisticated Text Analysis Is Hard, but it Works

Since its founding more than twenty years ago Elder Research has been involved in hundreds of data mining projects. Most of those projects employ numerical data, but for about a decade now we have been called on increasingly to extract information from unstructured or semi-structured text.  Though Gartner recently classified Text Analytics as just exiting the “Trough of Disillusionment” on their famous “Hype Cycle”,[1] we have found that every text mining project we have worked on has been a success.

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Prediction in the Public Sector: Why the Government Needs Predictive Analytics

Eric Siegel

June 22, 2018

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Data can appear lifeless and dull on the surface—especially government data—but the thought of it should actually get you excited. Data is the very most interesting and powerful thing. First off, data is exactly the stuff we bother to write down—and for good reason. But its potential far transcends functions like tracking and bookkeeping: Data encodes great quantities of experience, and computers can learn from that experience to make everything work better.

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Team Diversity: Women In Data Science

Paul Derstine

June 15, 2018

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According to Women in Tech: The Facts, a report by the National Center for Women & Information Technology (NCWIT), “In 2015, women held 57% of all professional occupations, yet they held only 25% of all computing occupations.”  The NCWIT report authors believe that this pattern is “especially troubling given ample evidence of the critical benefits diversity brings to innovation, problem-solving, and creativity. Indeed, a solid body of research in computing and in other fields documents the enhanced performance outcomes and benefits brought about by diverse work teams.”

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Share Your Case Study at Predictive Analytics World for Government

Paul Derstine

June 1, 2018

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The only conference of its kind, Predictive Analytics World for Government advances the deployment of analytics within federal, state and local government -- to drive smarter decisions, automate manual processes, and reduce fraud, waste, and abuse -- by extracting actionable insights from vast quantities of data. Are you interested in sharing your case studies, lessons learned, or best practices for using analytics to further your mission? 

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Developing an Analytics Strategy: The Analytics Champion

Robert Pitney

May 25, 2018

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As outlined in Developing an Analytics Strategy: The Role of Culture, there are five facets to “cultural infrastructure” that organizations must address to realize the full potential of analytics.  This may require significant cultural change, which must begin with executive leadership, i.e. setting the “tone at the top”.  We recommend appointing an Analytics Champion to maximize value from analytics. Here, I’ll describe the key traits of an Analytics Champion and how they can set up an organization for future “wins” with analytics.

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Why You Should Attend Predictive Analytics World

Paul Derstine

May 18, 2018

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This year there will be only ONE Predictive Analytics World conference in the U.S. Billed as Mega-PAW, it will be the largest Predictive Analytics World event to date and is the premier cross-vendor conference for machine learning and predictive analytics professionals, managers and commercial practitioners. The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail.

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