(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 checklist of ten practical actions that I’ve found usually help: Transform real-valued inputs to be approximately Normal in
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...
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...
(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...
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...
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