search for: tunerf

Just curious if anyone else has got this error before, and if so, would know what I could do (if anything) to get past it: > mtry <- tuneRF(training, trainingdata$class, ntreeTry = 500, stepFactor = 2, improve = 0.05, trace = TRUE, plot = TRUE, doBest = FALSE) mtry = 13 OOB error = 0.62% Searching left ... mtry = 7 OOB error = 1.38% -1.222222 0.05 Searching right ... mtry = 26 OOB error = 0.24% 0.6111111 0.05 mtry = 52...

Hi, i try several possibilities adn looking in the archive, but didn't getting success to install j.lindsey's usefuel "library repeated" on my linux (suse9.0 with kernel 2.6.7,R.1.9.1) P.S. Windows, works fine Many thanks for help Christian chris at linux:/space/downs> R CMD INSTALL - l /usr/lib/R/library repeated WARNING: invalid package '-' WARNING:

...liar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information, as well as the Fortran code. Comments/questions/bugs reports/patches much appreciated! A few notes about the new version: o There is a new tuneRF() function for searching for the optimal mtry, following Breiman's suggestion. PLEASE use it to see if result can be improved! o A new variable importance measure replaces the one based on margin. This new measure is the same as in Breiman's V5. The analogous measure is also implemente...

...liar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information, as well as the Fortran code. Comments/questions/bugs reports/patches much appreciated! A few notes about the new version: o There is a new tuneRF() function for searching for the optimal mtry, following Breiman's suggestion. PLEASE use it to see if result can be improved! o A new variable importance measure replaces the one based on margin. This new measure is the same as in Breiman's V5. The analogous measure is also implemente...

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]