similar to: Progress bar or execution plan for modeling process

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

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

2nd Workshop "Ensemble Methods" Max Planck Institute Tuebingen, Germany March 4-5, 2005 The second workshop on ensemble methods will take place at the Max Planck Institute Tuebingen (Germany) on March 4-5, 2005. This workshop is jointly organised by the German working groups "Computational Statistics" (IBS-DR) and "Statistical Computing" (GMDS) as well as the

The NOAA National Centers for Coastal Ocean Science is hiring a Computational Ecologist, a statistical/computational ecologist with experience fitting advanced spatial models to marine wildlife survey data (e.g., seabirds and marine mammal transects, fisheries trawl surveys) in R and other statistical languages. This is a full-time, long-term stable contract position. We are looking for an

Hi, I am trying to model a dataset with the response variable Y, which has 6 levels { Great, Greater, Greatest, Weak, Weaker, Weakest}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some

I need to get more digits in predicting a test sample with a random forests object. Format or options(digits=) do nothing. Any ideas? Thank you, Nagu

Hi, I am trying to run randomForests on a datasets of size 500000X650 and R pops up memory allocation error. Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library. Thank you, Nagu

Hi, I am using some classifiers in RWeka packages and met a couple problems. (1) J48 implements C45 classifier, the C45 should be able to handle missing values in both training set and test set. But I found the J48 classifier can not be evaluated on test set with missing values--it just ignore them. (2) The ensemble classifiers in RWeka such as bagging and boosting: there is a

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hello, I want to create a progress bar for the exececution of a particular function. I found a solution at http://brainspl.at/articles/tag/background but one need to create a separate server that communicates the progress of a job. Is there anyways to change a global variable or so and have the user see the execution stage of the function? I tried playing with Thread but no success because

Hi, I am using randomForests for a classification problem. The predict function in the randomForest library, when asked to return the probabilities, has precision of two digits after the decimal. I need at least four digits of precision for the predicted probabilities. How do I achieve this? Thank you, Nagu

https://bugzilla.samba.org/show_bug.cgi?id=2130 Summary: suppressing progress bar when not the foreground process Product: rsync Version: 2.6.3 Platform: All OS/Version: All Status: NEW Severity: normal Priority: P3 Component: core AssignedTo: wayned@samba.org ReportedBy: lukem@NetBSD.org

https://bugzilla.samba.org/show_bug.cgi?id=2130 ------- Additional Comments From lukem@NetBSD.org 2004-12-08 03:06 ------- Created an attachment (id=828) --> (https://bugzilla.samba.org/attachment.cgi?id=828&action=view) rsync-progress.patch -- Configure bugmail: https://bugzilla.samba.org/userprefs.cgi?tab=email ------- You are receiving this mail because: ------- You are the QA

https://bugzilla.samba.org/show_bug.cgi?id=2130 wayned@samba.org changed: What |Removed |Added ---------------------------------------------------------------------------- Status|ASSIGNED |RESOLVED Resolution| |FIXED ------- Additional Comments From wayned@samba.org 2005-02-13 21:40

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and