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It is a Mistake to…. Listen Only to the Data

 (Part 5 of 11 of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) Inducing models from data has the virtue of looking at the data afresh, not constrained by old hypotheses. But, while “letting the data speak”, don’t tune out received wisdom. Experience has taught this

The Data Audit Process (Part 1)-The Initial Step in Building Successful Predictive Analytics Solutions

 Building predictive analytics solutions is very much in-vogue for most organizations today. Historically, practitioners needed to educate businesses on the value of data mining and predictive analytics. Now, the concept and value of predictive analytics is widely...

10 Practical Actions that Could Improve Your Model

   (adapted from Chapter 13 of the Handbook of Statistical Analysis and Data Mining Applications) After a first pass of training and evaluating a model, you may find you need to improve its results.  Here is a...

The Great Analytical Divide: Data Scientist vs. Value Architect

 In the analytics space, it is quite common for many organizations to have a team of data miners who are now referred to as data scientists and a team of business users who are often referred to...

Employee Churn 202: Good and Bad Churn

 Our prior article on this venue began outlining the business value for solving “the other churn” – employee attrition. We introduced the “quantitative scissors” with a simple model of employee costs, benefit, and breakeven points. The goal...

Why Overfitting is More Dangerous than Just Poor Accuracy, Part II

 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....

Predictive Analytics is the Answer to Smart Fulfillment and Omni-Channel Retailing

 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...

Employee Churn 201: Calculating Employee Value

 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...

Why Overfitting is More Dangerous than Just Poor Accuracy, Part I

 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...

5 Ways to Become Extinct as Big Data Evolves

 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...

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