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How and Why to Interpret Black Box Models

Grant Fleming

March 27, 2020

BLOG_Holding Black Box Models Accountable Through Interpretability

Demand for data science services continues to accelerate, which has fueled the rapid development of ever more complex models. That complexity has contributed to the poor application of models and thus to controversy surrounding the true value of data science. It is vital for us as data scientists to ensure that, while our models continue to improve in performance, we can also interpret how they function, and thereby diagnose any harms that they might cause through biased or unfair predictions.

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What is the Value of Data Engineering?

William Proffitt

March 13, 2020

Data Engineering

With more organizations discovering the value of using data science to make better decisions, new opportunities are emerging for Data Engineers to provide support and integration for analytics teams. What’s valuable about Data Engineering skills?

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How to Pick a Winning March Madness Bracket

Robert Robison

February 28, 2020

BLOG_How to Pick a Winning March Madness Bracket

In 2019, over 40 million Americans wagered money on March Madness brackets, according to the American Gaming Association. Most of this money was bet in “bracket pools,” which consist of a group of people each entering their predictions of the NCAA tournament games along with a buy-in. The bracket that comes closest to being right wins. If you also consider the bracket pools where only pride is at stake, the number of participants is much greater. Despite all this attention, most do not give themselves the best chance to win because they are focused on the wrong question.

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Updating a Data Pipeline with AWS’s Latest Offerings

Todd Gerdy

February 14, 2020

 BLOG_Updating a Production Data Pipeline with AWS’s Latest Offerings

In December I attended AWS re:Invent, Amazon Web Services' annual learning conference. It was five days filled with over 4,000 sessions, keynote announcements, a partner expo, and hands-on training and certification opportunities. I learned about a number of tools and services (some brand new) that will improve the data pipeline solutions we develop for clients. This article describes a production pipeline solution and several options for improving it using these tools and services.

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Transform Your Business with Focused Analytics Training

Paul Derstine

February 3, 2020

 BLOG_Elder Research Acquires Statistics

Are you considering investing in sharpening the analytical skills of your staff? Do you wish that your IT, business, marketing, or operations group would more effectively employ predictive analytics in their work? Elder Research is excited to announce that it has acquired the Institute for Statistics Education at Statistics.com to provide focused data science, analytics, and statistics training for corporations and individuals. 

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Improve Predictive Model Performance With Ensembles

Jordan Barr, Ph.D.

January 17, 2020

BLOG_Improve Predictive Model Performance With Ensembles

In my previous blog, Ensembles and Regularization – Analytics Superheroes, I reviewed the many advantages of model ensembles including removing “noise” variables, generalizing better than single component models, and reducing sensitivity to outliers.

In this article I take a deeper dive into the attributes and applications of model ensembles, and explore potential downsides to provide context for when to use them.

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Trends in Natural Language Processing

Stuart Price, Ph.D.

December 27, 2019

BLOG_Trends in Natural Language Processing

Deep Neural Networks (DNN) have radically changed the landscape of state-of-the-art performance in Natural Language Processing (NLP) within recent years. These versatile models are being used in many applications including text classification, language creation, question answering, image captioning, language translation, named entity recognition, and speech recognition. The state-of-the-art is changing quickly, sometimes leading to large leaps in performance with the release of new architectures. In October of 2018 Google released BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding which performed best in 11 different NLP benchmarks upon release. Since then, there have been many more models adding new components or tweaking the approach. In this article we’ll review some of the traditional machine learning methods used in deep learning and new trends such as Transfer Learning and Transformers to provide a foundation no matter what model is currently leading.

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Modeling Outcomes: Explain or Predict

Peter Bruce

December 27, 2019

BLOG_Explain or Predict

A casual user of machine learning methods like CART or Naive Bayes is accustomed to evaluating a model by measuring how well it predicts new data.  When examining the output of statistical models, they are often flummoxed by the profusion of assessment metrics. Typical multiple linear regression output will contain, in addition to a distribution of errors (residuals) and root mean squared error (RMSE), values such as R-squared, adjusted R-squared, t-statistics, F-statistics, P-values, degrees of freedom, at a minimum, plus more.

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Making Good Use of Negative Space in Machine Learning

Will Goodrum, Ph.D.

December 13, 2019

BLOG_Making Good Use of Negative Space in Machine Learning

Data Scientists frequently build Machine Learning models to discover interesting (rare) events in data. These events can be valuable (e.g., customer purchases), costly (e.g., fraud), or even dangerous (e.g., threat). Finding them is a “needle-in-a-haystack” challenge: the events are rare and hard to distinguish from the huge mass of overwhelmingly uninteresting cases recorded. To differentiate rare from normal events it helps to have a good understanding of normal behavior. But, how well do you actually know the haystack?

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Ways Machine Learning Models Fail: Missing Causes

Mike Thurber

November 29, 2019

BLOG_Ways Machine Learning Models Fail - Missing Causes

I have identified five primary reasons why analytical models fail:

  1. Poor Organizational Support
  2. Missing Causes
  3. Model Overfit
  4. Data Problems
  5. False Beliefs

In this post, we will consider how and why missing causes in the data for training a model may result in incorrect inferences or failures.

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Leveraging Data Analytics to Increase ROI

John Elder, Ph.D.

November 15, 2019

BLOG_Leveraging Data Analytics to Increase ROI-1

Reluctance to trust and rely on machine-based decisions is widespread. That is understandable; how can one be sure the automated decision system takes into account all the factors it should? Employees struggle first to learn the new technology, and then after making great progress and producing a promising model, decision-makers can still prove extremely reluctant to risk a new approach, no matter how well tests reveal its effectiveness. Still, in today’s competitive work environment, having a positive relationship with machines is essential to increasing profits and building return on investment (ROI).

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Big Data and Clinical Trials in Medicine

Peter Bruce

November 1, 2019

BLOG_Big Data and Clinical Trials in Medicine

There was an interesting article in the New York Times magazine section on the role that Big Data can play in treating patients — discovering things that clinical trials are too slow, too expensive, and too blunt to find. The story was about a very particular set of lupus symptoms, and how a doctor, on a hunch, searched a large database and found that those symptoms were associated with an increased propensity for blood clots.

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Detecting Hidden Fraud Risk from Public Data

Hudson Hollister

October 18, 2019

BLOG_Detecting Hidden Fraud Risk from Public Data

Detecting which of the federal government’s millions of contracts1 most likely involve fraud used to require insider access to agencies’ IT systems. Data analytics provides greater efficacy and higher hit rate than traditional investigative methods – and now can even be performed using only public data.

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Be Smarter Than Your Devices: Learn About Big Data

Peter Bruce

October 4, 2019

BLOG_Be Smarter Than Your Devices-Learn About Big Data

When Apple CEO Tim Cook finally unveiled his company’s new Apple Watch in a widely-publicized rollout, most of the press coverage centered on its cost ($349 to start) and whether it would be as popular among consumers as the iPod or iMac. Nitin Indurkhya saw things differently.

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The Case for Government Investment in Analytics

Jane Wiseman

September 20, 2019

BLOG_Government Investment in Analytics

Government stands to gain $1 trillion globally from using data analytics.1 Few government data teams have the resources to document their value, but those that do can show as much as eight-to-one return on their cost. There is significant non-financial benefit as well, as public faith in government may improve when saving time and money is paired with increased transparency and accountability.  

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Determining Federal Grant Recipient Fraud Risk

Wayne Folta

September 6, 2019

BLOG_Determining Federal Grant Recipient Fraud Risk

Elder Research partnered with Excella Consulting to build an end-to-end grant risk estimation solution in the client’s AWS cloud. It used text mining and document classification to extract CPA Findings from audit reports and assign risk scores to federal grant recipients.

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Better NLP Models: OpenAI GPT-2

Peter Bruce

August 23, 2019

BLOG_Better NLP Models - OpenAI GPT-2

I’ve been told that, in conversation, I jump in and finish other people’s sentences for them. Now there’s an app for that: GPT-2 released by OpenAI, founded by Elon Musk. GPT-2 is a natural language program that, given a prompt, will write (mostly) intelligible content. OpenAI's stated mission is “to ensure that artificial general intelligence (AGI) … benefits all of humanity.” Natural Language Processing (NLP) includes applications such as text classification, language creation, answering questions, language translation, and speech recognition.

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Why Machine Learning Is the Coolest Science

Eric Siegel

August 9, 2019

BLOG_Why Machine Learning Is the Coolest Science

The absolutely coolest thing in science and engineering is machine learning, when computers learn from the experience encoded in data. I shall now support that hypothesis.

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Investment Modeling Grounded In Data Science

John Elder, Ph.D.

July 26, 2019

BLOG_Investment Modeling Grounded In Data Science

Elder Research has solved many challenging and previously unsolved technical problems in a wide variety of fields for Government, Commercial and Investment clients, including fraud prevention, insider threat discovery, image recognition, text mining, and oil and gas discovery. But our team got its start with a hedge fund breakthrough (as described briefly in a couple of books1,2), and has remained active in that work, continuing to invent the underlying science necessary to address what is likely the hardest problem of all: accurately anticipating the enormous “ensemble model” of the markets.

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The Persuasion Paradox – How Computers Optimize their Influence on You

Eric Siegel

July 5, 2019

DR. Data Show

How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence or persuasion, considering it’s so important? And what’s the ideal technique for optimizing your dating life and for getting more people to wash their hands in public restrooms? It’s time for Dr. Data’s persuasion paradox “Groundhog Day”-inspired geeksplanation.

Persuasion modeling requires a “deep geek dive” – but it’s as important as it is fascinating.


Note: This article is based on a transcript of The Dr. Data Show episode. View the video here.


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Healthcare Analytics: Exploration vs. Confirmation

Peter Bruce

June 21, 2019

BLOG_Healthcare Analytics_Exploration versus Confirmation

Perhaps the most active application of analytics and data mining is healthcare.  This week we look at one success story, the use of machine learning to predict diabetic retinopathy, one story of disappointment, the use of genetic testing in a puzzling disease, and a basic dichotomy in statistical analysis.

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Data is Not Oil. It is Land.

Will Goodrum, Ph.D.

June 7, 2019

Blog_Data is Not Oil, It Is Land-1

It has become common to talk about data being the new oil. But a recent piece from WIRED magazine points out problems with this analogy. Primarily, you must extract oil for it to be valuable and that is the hard part. Framing data as oil is not illuminating for executives trying to value their data assets. Oil is valuable, marketable, and tradable. Without significant effort, data is not. Data has more in common with land that may contain oil deposits than it does with oil.

Framing data as a real asset may help executives understand its value.

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Tiger vs. Jack – Asking the Right Questions

Daniel Brannock

May 24, 2019

BLOG_Tiger vs. Jack – Asking the Right Questions-1

One of the most fundamental contributions we can make as consultants is to help our clients ask the right questions of their data. We’re often asked to help solve problems that turn out to be too broadly or too narrowly defined—or are not aligned with achieving business goals or eliminating pain points.

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A Deep Dive into Deep Learning

Peter Bruce

May 10, 2019

BLOG_A Deep Dive into Deep Learning

On Wednesday, March 27, the 2018 Turing Award in computing was given to Yoshua Bengio, Geoffrey Hinton and Yann LeCun for their work on deep learning. Deep learning by complex neural networks lies behind the applications that are finally bringing artificial intelligence out of the realm of science fiction into reality. Voice recognition allows you to talk to your robot devices. Image recognition is the key to self-driving cars. But what, exactly, is deep learning?

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Discovering The Efficacy Of A New Drug

John Elder, Ph.D.

April 26, 2019

BLOG_Discovering The Efficacy Of A New Drug

The company had invested hundreds of millions of dollars investigating a new potential drug to treat a mental ailment, and they had zeroed in on a compound that showed promise but the compound was not passing the statistical tests required by the FDA. Elder Research was hired to examine the data and determine the drug’s viability. 

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