Category: Data Scientist
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AI and Productivity in Software Development-Thoughts and Comments
In my first post for the new synergy.science blog, I concentrate on a very recent article about the salient issue in an AI-driven labor and occupational restructuring that may be imminent in software development. This article is available for a no-charge download. Here are some details: “The Effects of Generative AI on High Skilled Work:…
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Sampling: New-School Approaches & Old-School Approaches
DRAFT UNDER LIVE CONSTRUCTION
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Model Management is a Team Activity
By Keith Schleicher, Data Science, Analytics, and AI/ML Leader While there is a wide array of personalities within the realm of Predictive Analytics/Data Science professionals, one would not use “extrovert” as a first word to describe a typical member of this profession. Most of us went into this line of work because we love to…
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Data Reliability and Validity, Redux
August 1 2023 By Dr. Bill Luker (Synergy Data Science) Here is a recent post, from the vast LinkedIn commentariat, that raises the often neglected issue of reliability and analytic validity (R&V) in survey data. Since survey data constitutes such a huge proportion of all data collected by business and academic research scientists, it’s important.…
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What is Multicollinearity? Why Does it Matter? When can it be Ignored? What Models are Affected? How is it Diagnosed and Corrected?
Dr. Gordy Fairchild (CEO of Synergy Data Science) Misinformation about multicollinearity is commonplace. In short, there is disagreement among practitioners about many facets of multicollinearity. Both new-school and old-school data scientists often handle it in different ways. Inexperienced new-school practitioners who have been trained quickly in a data science boot camp typically do not have…
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How do you set the optimal cut-off (probability threshold) for classifiers without blindly using the default of 0.5?
Setting probability thresholds in classification models depends on the specific requirements of your problem and the trade-offs you are willing to make between precision and recall (aka sensitivity or the True Positive Rate. In binary and categorical classification, the model typically outputs a probability score for each class, and a threshold is applied to determine…
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What’s in a definition: AI, ML, Data Science, Statistics, Analytics, and Econometrics?
Dr. Gordy Fairchild & Poe AI. While there is some overlap among these terms, they represent distinct fields and approaches. Here’s an overview of the differences among the terms “data science,” “artificial intelligence” (AI), analytics, statistics, “machine learning,” and econometrics. It should be noted that to some practitioners AI includes all the other terms as…