Why you need more data engineers, but not for the reasons you think The role of the data scientist has evolved quite a bit over the last few years. While in some areas, it stemmed from groups of software engineers and other IT specialist who soon realized making models was more than linking to a

Visualize Clustering with SOM in Anatella / R Note: The data is extracted from Marketing Engineering, with the kind permission of Dr A. De Bruyn. A very complete R code for SOM can be found in the excellent post of Shane Lynn http://www.shanelynn.ie/self-organising-maps-for-customer-segmentation-using-r/ Clustering is a tricky business. while the hardest part of it lies

Classification problems: lift curve or classification table? The common idea of classifying is to look at “small groups” of records, and evaluate if we should put them a 1 or a 0 when it comes to a particular target. For example, if I am interested in figuring out who will get cancer, I can “build”

Lift, ROC, AUC and Gini One good way to compare different predictive modeling platforms is to compare the models that are produced by these platform.Comparing models across platform is not an easy task. Models can be compared using various criteria’s: 1.    Simple predictive Model Quality (i.e. Height of the lift curve / AUC) 2.    Generalization