Industry leader and consultant Geert Verstraeten serves as program chair for Predictive Analytics World London. Click here for information about speaking at PAW London as well as 9 other annual PAW events. Granted, the fear of public speaking is often considered the most common of all phobias. In some (non-scientific) studies, evidence suggests that people
Defining a target variable is one of the preliminary steps in building a predictive model. But is this really a simple and straightforward process. For example, I want to optimize insurance sales to existing customers on a...
Industry leader and author Dean Abbott will be presenting at Predictive Analytics World Boston (Oct 5 – 9) on “Data Preparation from the Trenches: 4 Approaches to Deriving Attributes.” Abbott will also run two post-conference full-day...
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...
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...
(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...
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