search for: gettree

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...

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 informatio...

...ssfully build a model. I'd like to go one step further however, and use the output from the model to construct a map using the output. My input data are spatial, and I have an independent set of rasterize maps for each of the predictor variables, to map the classification solution. Using the getTree function: > getTree(randomForest(veg.physical[,-1], veg.physical[,1], ntree = 20, ), labelVar = TRUE) returns a "list" with dim(tree) 451 rows x 6 columns. I assume that other folks using this package and publishing maps have used this list to link the left and right daughters, sp...

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, as.factor(class),type="CART", ntree=50, nforest=10, mtry=5) getTree(learn.cart.keep, k=3) detach(srbct) It would be very convenient to have a Weka Decision Tree object...

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 Inf in "V7" would be helpful? Th...

...olution [simplified!]: ## First I fit the tree to my Data, all in C, but only return the *size* ## I'll need to store that tree in R size <- .C("fittree", data, ..., res = integer(1))$res ## Now that I know its size, I can get it into R : tree.in.R <- .C("gettree", size, res = integer(size))$res where in the C code, I have a global static *tree_struct my_tree; and in C's fittree(), I use my_tree = Calloc(1, tree_struct); for building the tree, which is then accessed by gettree(), which ends in Free(my_tree); All well, BUT: proble...

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!...

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

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]]

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

...resentations of rpart or randomForest, I came to a much simpler and straight-forward solution: with only a couple of lines, the corresponding c-code for a tree can be emitted. e.g. for randomForest: ## generate C code from rforests. generate code for tree k gen_code=function(rfobj,k) { tree=getTree(rfobj,k,labelVar=TRUE); count=0; loc.env=environment(); visit=function(idx) { assign('count',count+1,env=loc.env); if (tree[idx,'status']==-1) cat(sprintf("RETURN(%s)\n",tree[idx,'prediction'])) else { cat(sprintf("if (GET...

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

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 are named. Any help would be appreciated. I'm running R2.10.0 on windows XP. Ki...

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:

...there are 950 class 1s and 50 class 2s, use sampsize=c(50, 50) will usually give a more `balanced' classifier. o There is a new importance() function for extracting the importance measure. o The predict() method has an option to return predictions by the component trees. o There is a new getTree() function for looking at one of the trees in the forest. o For dealing with missing values in the predictor variables, there are na.roughfix() and rfImpute(), which correspond to the `missquick' and `missright' options in Breiman's V4/V5 code. Both works for classification as well...

...there are 950 class 1s and 50 class 2s, use sampsize=c(50, 50) will usually give a more `balanced' classifier. o There is a new importance() function for extracting the importance measure. o The predict() method has an option to return predictions by the component trees. o There is a new getTree() function for looking at one of the trees in the forest. o For dealing with missing values in the predictor variables, there are na.roughfix() and rfImpute(), which correspond to the `missquick' and `missright' options in Breiman's V4/V5 code. Both works for classification as well...

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