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Elder Research Data Science and Predictive Analytics Blog

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Elder Research Data Science and Predictive Analytics Blog

Rated: 2.33 / 5 | 6,095 listing views Elder Research Data Science and Predictive Analytics Blog Blogging Fusion Blog Directory

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  • Paul Derstine
  • March 15, 2017 12:52:18 AM
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A Little About Us

Elder Research is a recognized leader in the science, practice, and technology of advanced analytics. Topics on our blog cover analytics tips, analytical modeling, data and text mining tools, data visualization, analytics best practices, case studies, etc. to provide business leaders with actionable information on real world analytics problems.

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    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...

    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?


    Ways Machine Learning Models Fail: Missing Causes

    I have identified five primary reasons why analytical models fail: Poor Organizational Support Missing Causes Model Overfit Data Problems 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....

    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.


    Leveraging Data Analytics to Increase ROI

    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,...

    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).


    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....

    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.


    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...

    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.


    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....

    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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