• If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • Whenever you search in PBworks, Dokkio Sidebar (from the makers of PBworks) will run the same search in your Drive, Dropbox, OneDrive, Gmail, and Slack. Now you can find what you're looking for wherever it lives. Try Dokkio Sidebar for free.



Page history last edited by mike@mbowles.com 10 years, 4 months ago

Reading for Gradient Boosting Machine


Basically, we'll read a series of three papers in order to get a complete understanding of gbm.  The sequence follows the temporal order in which these papers were published and will allow us to follow the development of the idea.  In order, here are the papers. 

1.  additiveLogisticRegression-Boosting.pdf

2.  greedyFunctionApprox-2001.pdf

3.  stochasticGradientBoosting-2002.pdf


Also look at the gbm package guide gbmPackageGuide.pdf  This has got some general explanation of gbm in addition to descriptions of the packages features and options. 


Here's some introductory code Ensemble.R

Here's R-code for the in class iris example:  gbm.R

Here's gbm set up to classify the sonar data  clemGBM.R


Recorded Lecture:

Part 1.  https://datamining.webex.com/datamining/ldr.php?AT=pb&SP=MC&rID=116220557&rKey=9a5e9ca981ffda75


Part 2.  https://datamining.webex.com/datamining/ldr.php?AT=pb&SP=MC&rID=116220967&rKey=7090edfaf9bd1b4e

Comments (0)

You don't have permission to comment on this page.