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This article will make you feel better. And you do need to feel better,...
Too many people confuse reporting with analytics–and underinvest in making sure they are asking...
In part one, I described one problem with overfitting the data is that estimates of the target variable in regions without any training data can be unstable, whether those regions require the model to interpolate or extrapolate. Accuracy is a problem, but more precisely, the problems in interpolation and extrapolation are not revealed using any […]
Over the past 5 years there have been several trends that have changed the way retailers operate their businesses. Many of them have to do with how consumers use technology to make a purchase. Pure e-commerce retailers have gained momentum causing brick-and-mortar retailers to re-think and re-design their online channels. Meanwhile, mobile technology has surged […]
Much has been written about customer churn – predicting who, when, and why customers will stop buying, and how (or whether) to intervene. Employee churn is similar – we want to predict who, when, and why employees will terminate. In many ways, it is smarter to to focus inward on employees. For one thing, it […]
Arguably, the most important safeguard in building predictive models is complexity regularization to avoid overfitting the data. When models are overfit, their accuracy is lower on new data that wasn’t seen during training, and therefore when these models are deployed, they will disappoint, sometimes even leading decision makers to believe that predictive modeling “doesn’t work”. […]
The need to adopt sophisticated data analytics has become widely apparent to businesses recently, and the necessity of adopting “Big Data” analytics approaches is only becoming more evident. Gartner’s report on Big Data Adoption in 2013 found that 64 percent of organizations have already invested in or plan to invest in Big Data technology, and […]
(Part 4 (of 11) of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) It is very important to have the right project goal; that is, to aim at the right target. This was exemplified (in a positive way) by a project at Shannon Labs, led by […]
Network analysis is an emerging Business Intelligence technique that’s increasingly used in risk management, social network analytics, banking, telecommunication analytics, bioinformatics, criminal intelligence, and human resources planning. Sometimes the term Network Analysis (or Network Analytics) is mixed up with neural network modeling, which is a commonly used data-mining algorithm for various tasks (telephone-circuit fault diagnosis, […]