Predictive Analytics Times
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Recognizing and Avoiding Overfitting, Part 1

 In my last two posts I described why overfitting predictive models is dangerous beyond the most obvious problem, namely that accuracy on new data is lower than expected. In the next few posts, I’ll describe how to recognized that overfitting may be occurring, and some common approaches to remove or mitigate the effects of overfitting.

Webinar: Towards Solving Employee Attrition: Cost Modeling

 Presented by: Pasha Roberts, Chief Scientist, Talent Analytics, Corp. Watch Webinar. Pasha Roberts, Chief Scientist at Talent Analytics, Corp., discusses Talent Analytics’ first step when using a predictive analytics approach for solving employee attrition challenges. Severe employee...

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...

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...

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