similar to: Help with RandomForest

What does a status value of -3 mean when I do a regression with RF and use the getTree function? left daughter right daughter split var split point status prediction 1 2 3 11 4.721000e+03 -3 15.8489576 2 4 5 5 6.500000e+00 -3 11.3240895 3 6 7 10 6.790674e+02 -3 23.7250079 4

Hi, >> I am interested in using R for machine learning (supervised classification). >> Currently, I have been investigating especially the rpart, tree, and randomForest package, and have >> achieved first results. >> >> are there any experiences, how the learned classificators could >> be used in e.g. C ? >> in other words, I want to

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi All, In the script below, the importance measure for column 4 (ie MeanDecreaseGini) indicated "Inf" for V7. Running the getTree command showed that "V7" had been selected at least twice in one of the trees for Random Forest. So the "Inf" command was not generated as a result of dividing the sum of the decreases by 0. Any suggestions on what may be causing the

All, What purpose does the getTree function have in Random Forest? Can you graph it as you can in rpart and can it be interpreted in the same way? Helen Mills Poulos Yale School of Forestry

Hello, This is a re-submittal of question I submitted last week, but haven't rec'd any responses. I need to extract the probabilities used to construct the barplots displayed as part of the graph produced by plot("ctree"). For example, library(party) iris.ct <- ctree(Species ~ . , data = iris) plot(iris.ct) Instead of a simple example with only 4 terminal nodes, my

[sending this to R-devel, since it may be of general interest and could possibly lead to better instructions in the "R-exts" manual] Is this another case where only .Call() or .External() [or a fudge factor] solve my problem? Following situation: I use dyn.loaded C code to compute a recursive tree structure from my data. Beforehand I don't know how big the tree will

Hello Trying to develop variograms and kriged surfaces from a point file. Here is what I've done so far. library(gstat) # also loads library(sp) library(lattice) soilpts$x <- soilpts$UTM_X soilpts$y <- soilpts$UTM_Y soil.dat <- subset(soilpts, select=c(x, y, Area, BulkDensity, LOI, TP, TN, TC, Total_Mg)) dim(soil.dat) [1] 1292 7 coordinates(soil.dat) <- ~ x+y

Dear all, I am working with randomForest package and I am interested in examining the "Gini importance" measures that are used as a general indicator of feature relevance. Is there a possibility of getting the Gini measure that is being estimated in each tree by the output of the getTree() function? Thanks a lot, Chrysanthi [[alternative HTML version deleted]]

Hi, I would like to visualize a tree extracted from a random forest using getTree {randomForest}. I'm wondering if there is any way to do it directly or to convert my tree to any other class of tree repesentation, which then can be plotted? Regards, Umang Rathi ________________________________ This email message may contain proprietary, private and confidential information. The information

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

Hello, I am a phd student in Bioinformatics and I am using the Random Forest package in order to classify my data, but I have some questions. Is there a function in order to visualize the trees, so as to get the rules? Also, could you please provide me with the code of "randomForest" function, as I would like to see how it works. I was wondering if I can get the classification having

I see the function "getTree", which is very interesting. As I'm trying to teach myself more and more about R, and dealing with lists, it occurred to me that it might be fun to remove (as in delete) a single tree from a forest...say to go from 500 to 499. I know, I know... "why?" Why, to play, of course! I've been doing a lot of reading on various tuning parameters,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

The following two patches fixes / reimplements the oVirt appliance project, installing the entire oVirt stack including all server and node components on one machine. These patches are intended to be checked out and used to build the appliance rpm, after which it is installed provides the /usr/sbin/ovirt-appliance-ctrl script to install/uninstall the appliance. The first patch merely removes

Dear List, I'm trying to find a way to extract the individual conditional inference trees from cforest ( a modelling function in the party package) in a manner analogous to getTree in randomForest and I'm struggling. I can see that the information is held within the ensemble list, but haven't been able to work out how this sequence of nested lists is structured or if any of the items

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:

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am