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Blog Directory ID: 31267 Get VIP Status?
Google Pagerank: 1
Blog Description:

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.
Related URL: Elder Research Analytics & Data Science Whitepapers

Download white papers focused on providing information on the practical application of advanced analytics to uncover actionable insight to solve real-world business problems.
Related URL: Case Studies - Elder Research

Elder Research predictive analytics and data science consultancy case studies available for download.
Related URL: eBooks - Elder Research

Elder Research predictive analytics and data science consultancy eBooks available for download.
Blog Added: March 14, 2017 06:52:18 PM
Premium Bronze Membership: Never Expires   
Audience Rating: General Audience
Blog Platform: WordPress
Blog Country: United-States/Virginia   United-States/Virginia
Blog Stats
Total Visits: 1,169
Blog Rating: 3.50
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Statistical & Cognitive Biases in Data Science: What is Bias?

This is the first in a series of short blog posts where we explore common varieties of bias that can beset analytics projects. Bias has serious ramifications for the success of analytics in any organization. Understanding the nature of bias is crucial for understanding the extent of a model’s accuracy. In this first post, we discuss what bias is, why it occurs, and why it matters (a lot).


Transforming Business: Why Do We Stop Asking Why?

I’ve lived through this phenomenon first hand. The environment was new to me, sitting at my assigned seat at the cherry wood conference table for the weekly executive staff meeting. I was told very clearly that I was to stick to the presentation, answer only when asked a direct question, and never, no matter what happens, ask why! After being ushered out of the meeting when I finished, we quickly huddled for a post-meeting debrief. Everyone started asking “How do you think it went?” “What do you think he meant when he said this?” and “Did you understand what he asked us to do?” I finally asked, “Why didn’t we just ask him?”


The Nuances of Model Interpretability

There is growing literature around interpretable machine learning and explaining black box outputs to humans who will make real decisions based on the results. Predictive model interpretability is a nuanced and complex subject. For example, AlphaGO, the experimental deep learning solution created by Google to play the ancient board game Go, made headlines recently for defeating a Go grandmaster for the first time. This was a significant milestone for a machine learning system since Go is significantly more complex than chess.  When Go masters took an interest in AlphaGO’s winning strategy, the program’s creators faced a familiar question: Why did it choose certain moves?


Avoiding the Most Pernicious Prediction Pitfall

Data science and predictive analytics’ explosive popularity promises meteoric value, but a common misapplication readily backfires. The number crunching only delivers if a fundamental – yet often omitted – fail-safe is applied.


Building Models Experts Will Trust

There are two problems with humans making decisions from data. We are biased— even experts are just as likely to give inconsistent judgments—and we don’t always understand, or trust, the model. Although decision-makers could benefit from using data as a part of their decision making, raw machine learning results may not be meaningful enough.  So how can we use data in a way that experts trust without diluting the machine learning process?


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