similar to: Decision Tree in Python or C++?

I have found an existing image on Amazon EC2 including R. But unfortunately, it is 32-bit R on 32-bit Linux. Does anybody know if there exists an mage (R 64-bit on Linux 64-bit) on Amazon EC2? Or how can I install 64-bit R on my own Linux instance there? Thanks. -- View this message in context:

I have a 64-bit windows box - Intel Xeon CPU E7340 @ 2.4GHz 31.9GB of RAM I have R 2.11.1 (64bit) running on it. My csv data is 3.6 GB (with about 15 million obs, 120 variables.) ------------------------------------------------ I have successfully imported the data above into R. No problem. Now I am trying to run 'rpart' on my data. But I got the following error : Error: cannot

Dear useRs: I try to plot an rpart object but cannot get a nice tree structure plot. I am using plot.rpart and text.rpart (please see below) but the branches that connect the nodes overlap the text in the ellipses and rectangles. Is there a way to get a clean nice tree plot (as in the Rpart Mayo report)? I work under Windows and use R2.11.1 with rpart version 3.1-46. Thank you. Tudor ...

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

My decision tree grows only with one split and based on what I see in E-Miner it should split on more variables. How can I adjust splitting criteria in R? Also is there way to indicate that some variables are binary, like variable Info_G is binary so in the results would be nice to see "2) Info_G=0" instead of "2) Info_G<0.5". Thank you in advance! And thanks for Eric who

HI, Dear R community, How to extract the variables actually used in tree construction? I want to extract these variables and combine other variable as my features in next step model building. > printcp(fit.dimer) Classification tree: rpart(formula = outcome ~ ., data = p_df, method = "class") Variables actually used in tree construction: [1] CT DP DY FC NE NW QT SK TA WC WD WG WW

I'd like to train a decision tree on a set of weighted data points. I looked into the rpart package, which builds trees but doesn't seem to offer the capability of weighting inputs. (There is a weights parameter, but it seems to correspond to output classes rather than to input points). I'm making do for now by preprocessing my input data by adding multiple instances of each data

I am using "plot" and "text" commands to display the decision tree I built, here is the code: plot(fit, compress=TRUE) text(fit, use.n=TRUE) but the the result of this code is not readable. Text doesn't get fully displayed (missing on the margines and overlapping in the middle). Where can I get info on how to adjust the tree display and text size display? Please help.

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see anything yet. Mike [[alternative HTML version deleted]]

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Hi! I'm trying to construct and plot a decision tree to class a set of only 8 objects and tried to use the rpart and tree function, but get a error message both times: rpart: fit is not a tree, just a root tree: cannot plot singlenode tree I read in the post 'question regression trees' that rpart doesn't split a set of fewer than 20 objects...so I guess the same holds true for

Dear mailinglist members, I have the following problem: I run a decision tree using the rpart function and, afterwords, I try to find to which leaf node a new register (not used to build the decision tree) belongs to. I will try to explain better: rpart.tree <- rpart(target.value ~., data) leaf.node <- new.function(rpart.tree, new.register) The new register has all the explanatory values

Tjats great that you are familiar and thanks for responding. Have you ever done what I am referring to? I have alteady spent time going through links and tutorials about decision trees and random forrests and have even used them both before. Mike On Apr 13, 2016 5:32 PM, "Sarah Goslee" <sarah.goslee at gmail.com> wrote: It sounds like you want classification or regression trees.

Hi netters, I want to learn a decision tree from a series of instances (learning data). The packages tree or rpart can do this quite well, but the scoring functions (splitting criteria) are fixed in these packages, like gini or something. However, I'm going to use another scoring function. At first I wanna modify the R code of tree or rpart and put my own scoring function in. But it

I still need the output to match my requiremnt in my original post. With decision rules "clusters" and probability attached to them. The examples are sort of similar. You just provided links to general info about trees. Sent from my Verizon, Samsung Galaxy smartphone<div> </div><div> </div><!-- originalMessage --><div>-------- Original message

I am new in using R. I want to know all about building decision tree model in R. Few options which I searched are rpart and rattle to build a decision tree.Both the functions are giving me splits which are statistically appropriate. But I am not able to figure out how to change those splits as per my business requirement. for example : the automatic split of Age by using rattle is > 30 and

Hello, I'm building a decision tree in R with the rpart package. Modeling is fine. But when it comes to scoring, I have the following issue: factor 'cust_language' has new level(s) OT I think this comes from the fact that when learning, the DT doesn't see all the possible value of the factor variable cust_language. When scoring, new values comes and I get this error. However, it

Hi , I have built decision tree using rpart . I want to do k Fold validation on the decision tree . Could you help how can i do that .. please tell the package which required for K fold validation. Regards, Santosh -- View this message in context: http://r.789695.n4.nabble.com/Need-Help-in-K-fold-validation-in-Decision-tree-tp4630730.html Sent from the R help mailing list archive at

Hi, I am somewhat new to R so this question may be foolish, but is it possible to add decision trees into a list, array or vector in R? I am trying to build a collection (ensemble) of decision trees. Every time a new instance arrive I need to get the prediction of each decision tree. I have tried to add a decision tree into a variable but without luck. Is a special package needed perhaps? This