I would like to start off by congratulating StatSoft on the recent results for the Gartner Magic Quadrant for Advanced Analytics:

http://www.statsoft.com/Company/About-Us/Reviews/2014-Published-Reviews#gartner-adv-MQ2014

In the last blog I revisited the quality control method I had first talked about back on October 6, 2013.  I would like to wrap things up by talking briefly about the use of boosted trees for predictive quality control.  Anyone that has used boosted trees in STATISTICA Data Miner knows that they can be a very powerful technique for predictive modeling.  They can also be used for variable selection which makes them useful for predictive quality control.

In the following video I will demonstrate how to use boosted trees to get an importance plot with the machine data set provided previously.  I will also discuss how the results compare to the other two methods presented in previous posts and some tips on how to get the best results.