similar to: Decision Trees RWeka

Hi, OLPC's Openfirmware (OFW) implementation chokes when attempting to boot an iso created by isohybrid. The problem is that OFW's ISO-9660 support expects a partition type of 0x96 (CHRP ISO-9660, according to http://en.wikipedia.org/wiki/Partition_type). I see from

I'm working with the randomForest package and have successfully 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: >

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 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

[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

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]]

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

Hello everybody I'm having some difficulties to design the decision tree algorithm J48. I am using the following code and when I run it gives me the following message plot(m1) Error in plot.Weka_tree(m1) : Plotting of trees with multi-way splits is currently not implemented. #The code library(RWeka) library(randomForest) library(party) if(require(mlbench, quietly = TRUE) &&

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 Sir or Madam: I am Shaojuan Liao, the 3rd year Ph.D. student from Econ Department, Virginia Tech. I don't know whether it is appropriate to ask you questions on the command pgmm. But I don't know how to deal with the case where all X are exogenous and all T time periods' X can be used as the instrument. Problem 1: I know when X are predetermined, such as Z=[y1,X1,X2, 0,

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

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:

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 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

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 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

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,