When building models, data scientists and statisticians often talk about penalty, regularization and shrinkage. What do these terms mean and why are they important? According to Wikipedia, regularization "refers to a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information usually
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Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
A previous post, Spatial econometric modeling using PROC SPATIALREG, introduced the SAS/ETS® SPATIALREG procedure and demonstrated its usage to fit both linear and SAR models by using 2013 county-level home value data in North Carolina. In most analysis for spatial econometrics, you rarely know the true model from which your data
I am often asked to describe my career as a woman in analytics and provide some insights to guide women who wish to be part of this field and to succeed as leaders in the profession. I have divided my comments on women in analytics into sections, starting from the beginning,
I recently met Mrs. Claus at the INFORMS Annual Meeting, where we got to talking about the social network analysis session she’d just attended. It turns out Mrs. Claus and I are both fans of a book by Alex Pentland, Social Physics: How Social Networks Can Make Us Smarter. Apparently
In honor of today’s #GivingTuesday, which "harnesses the potential of social media and the generosity of people around the world to bring about real change in their communities,” I’ve been thinking about what constitutes “real change” and the role analytics can play on the many social issues our planet faces.
When shopping for a new TV, with many sets next to each other across a store wall, it is easy to compare the picture quality and brightness. What is not immediately evident and expected is the difference between how the set looked in the store and how it looks in your
Who says machine learning can't be fun? A crew of us from SAS went to San Francisco for the recent KDD conference, which bills itself as "a premier interdisciplinary conference, [which]brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data." We brought
The internet of medical things, spurred by the advent of wearable sensors, has dramatic consequences in industry, healthcare, and analytics, just as the advent of the internet of things and analytics has consequences in education. When I began my internship at SAS in May, I knew little about the internet of
Time series machine learning techniques show great promise for the analysis of health care wearable data. As our busy lifestyles render continuous monitoring more and more essential, the need to analyze data to find correlations between these data streams becomes even more important, because they can provide important cues to