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 attrition can be a thing of the past. It need not be the “cost of doing business”. Talent
(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...
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...
(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...
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...
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...
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....
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...
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...
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...
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