similar to: Webinar: Advances in Gradient Boosting: the Power of Post-Processing. TOMORROW, 10-11 a.m., PST

Webinar signup: Advances in Gradient Boosting: the Power of Post-Processing December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: * Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? * Applications Benefiting from

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

The Evolution of Regression: An Upcoming Webinar Series (Hands-on Component) Registration: http://bit.ly/salford-systems-regression-webinar-series Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series address these problems. Are you are working with larger datasets? Is your data challenging? Does your data include missing

Begins Friday: The Evolution of Regression: An Upcoming Webinar Series (Hands-on Component) Registration: http://bit.ly/salford-systems-regression-webinar-series Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series address these problems. Are you are working with larger datasets? Is your data challenging? Does your data

Maybe you missed Part 1 of "The Evolution of Regression Modeling from Classical Linear Regression to Modern Ensembles " webinar series, but you can still join for Parts 2, 3, & 4 Register Now for Parts 2, 3, 4: https://www1.gotomeeting.com/register/500959705 Download (optional) a free evaluation of the SPM software suite v7.0 (used in the hands-on components of the webinar). As a

Hands-on Webinar (no charge) Advances in Regression: Modern Ensemble and Data Mining Approaches **Part of the series: The Evolution of Regression from Classical Linear Regression to Modern Ensembles Register Now for Parts 3, 4: https://www1.gotomeeting.com/register/500959705 **All registrants will automatically receive access to recordings of Parts 1 & 2. Course Abstract: Overcoming Linear

Tomorrow, Friday March 15 Maybe you missed Part 1 of "The Evolution of Regression Modeling from Classical Linear Regression to Modern Ensembles " webinar series, but you can still join for Parts 2, 3, & 4 > Register Now for Parts 2, 3, 4: https://www1.gotomeeting.com/register/500959705 > > Course Outline: Overcoming Linear Regression Limitations > > Regression is

Hi, I'm using R to treat a table (with a lot of missing values) with Rulefit. The matter is when I use the command rfmod. Actually, I don't know how to deal with the error message. I don't know were "true" or "false" is missing. Someone can help me? Thanks The following part is the script I used with the error at the end. platform = "windows" rfhome

Hello LIST, In using Rulefit I;ve bee nabel to fit a model using rulefit without incident. However, when trying to use intnull and interact things go astray. > rf=rulefit(x,"N", cat.vars="H", > not.used=c("G","T"),huber=0.9,path.speed=1); [snip] RuleFit model 2/26/2008 2:17p ave(abs(error)) terms path steps 84.16 110

R version 2.8.1 (2008-12-22) on Linux 64-bit I am trying to run 'rulefit' function (Rule based Learning Ensembles). but I got the following error - > rulefit(x,y) Warning: This program is an suid-root program or is being run by the root user. The full text of the error or warning message cannot be safely formatted in this environment. You may get a more descriptive message by running

Greetings - if you're curious about monitoring GlusterFS performance, be sure and sign up for tomorrow's webinar. We will also post the recording online should you not be able to make it. Introducing Gluster for Geeks Technical Webinar Series In this Gluster for Geeks technical webinar, Craig Carl, Senior Systems Engineer, will explain and demonstrate how to monitor your Gluster

I'll be holding a libguestfs live ?webinar? tomorrow, Friday, 18th November 2011 at 16:00 UTC. To convert the date and time to your timezone, do: date -d '2011-11-17 16:00Z' The programme will include: - An introduction to libguestfs features. - Live demonstrations of guestfish, a Python program, inspection, and image resizing. - An overview of major new features in RHEL 6.3.

If I have a column with 2 levels, but one level has no remaining observations. Can I remove the level? Had intended to do it as listed below, but soon realized that even though there are no observations, the level is still there. For instance summary(dbs3.train.sans.influential.obs$HAC) yields 0 ,1 4685,0 nlevels(dbs3.train.sans.influential.obs$HAC) yields [1] 2 drop.list <- NULL

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

Hi all, Could you please give me some pointers about what's the best boosting package in R currently? in terms of classification accuracy? And any pointers about tutorials and study-materials to curb the learning curve will be greatly appreciated! Thank you! p.s. Does anybody happen to know Boosting implemented in other language such as Matlab? Are they good in terms of accuracy? What

Hi List, I can't seem to find a package that implements boosting for multi-class classification. Does such a package exist?

Hi, Dear R community, Does anyone know how to constructdecision tree with boosting? Is any tutorial I can read? -- Sincerely, Changbin -- [[alternative HTML version deleted]]

Hi, I am trying to implement the Adaboost.M1. algorithm as described in "The Elements of Statistical Learning" p.301 I don't use Dtettling 's library "boost" because : - I don't understande the difference beetween Logitboost and L2boost - I 'd like to use larger trees than stumps. By using option weights set to (1/n, 1/n, ..., 1/n) in rpart or tree