similar to: how to measure accuracy of regression tree?

Hello, I am using plot( ) function to plot time-series. it takes time-series object as an argument but i want to plot predicted data with training set, to compare them. is there any function available? Vibha [[alternative HTML version deleted]]

I just downloaded version 1.0.0 and several binary libraries (VR, rpart, norm, stataread) - WinNT version. I then converted a file from Stata 6.0 to R format by using the stataread library. The file converts perfectly and I was able to use the VR function lda on the dataframe without difficulty. I then tried to use the same dataframe with RPART. The model statement:

I've been using the package rpart with R 1.3.0 for Windows to produce simple classification trees for some measurement data from paleontological specimens. Both the rpart documentation and the output confirm that the program produces splits on continuous data that leave "holes" in the data. It is probably of little practical importance, but is there a reason why the binary

To whomever it may concern, I'm a young Industrial Engineer working on Senior Design at Georgia Tech and have found the StructTS method to be excellent for the training set for my forecasting project. There's only one problem: I don't actually understand what a Structural Time Series IS. I've looked up resources on it, and get that essentially you're dividing the Time

I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-

Hello all, When building a CART model (specifically classification tree) using rpart, it is sometimes interesting to know what is the importance of the various variables introduced to the model. Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

> I'm using rpart function for creating regression trees. > now how to measure the fitness of regression tree??? > > thanks n Regards, > Vibha I read R-help as a digest so often come late to a discussion. Let me start by being the first to directly answer the question: > fit <- rpart(time ~ age +ph.ecog,lung) > summary(fit) Call: rpart(formula = time ~ age +

I am trying to create a classification tree using either tree or rpart but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials. What I would like to see is the XX/XX format but all I get is a weird decimal value. I was also wondering how you know which is yes and which is no in each leaf of the tree? Is yes always on the left?

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.rpart)

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 ...

Hello helpers I have this problem. When I plot a regression tree, some words are cutted in the figure. There is an attached file tree.ps to see what I'm saying. In the right figure some labels are cutted. How can I solve this problem? -- http://adsl.sapo.pt -------------- next part -------------- A non-text attachment was scrubbed... Name: tree.ps Type: application/postscript Size: 4743

I am trying to make a decision tree using rpart. The function runs very quickly considering the size of the data (1742, 163). When I call the summary command I get this: > summary(bookings.cart) Call: rpart(formula = totalRev ~ ., data = bookings, method = "class") n=1741 (1 observation deleted due to missingness) CP nsplit rel error 1 0 0 1 Error in yval[, 1] :

Hi, when I have made a decision tree with rpart, is it possible to "apply" this tree to a new set of data in order to find out the distribution of observations? Ideally I would like to plot my original tree, with the counts (at each node) of the new data. Reagards, Jay

Hi, all, I am wondering if it is possible to set parameters of 'rpart' and 'tree' such that they will produce the exact same tree? Thanks. Auston Wei Statistical Analyst Department of Biostatistics and Applied Mathematics The University of Texas MD Anderson Cancer Center Tel: 713-563-4281 Email: wwei@mdanderson.org [[alternative HTML version deleted]]

Hi, I am using the rpart package to build a classification tree. I did manage to build a tree with data on a previous project. However, when attampting to build a tree on a project I am working on, I seem to be getting the error shown below: > nhg3.rp <- rpart(profitresp ~., nhg3, method="class") > plot(nhg3.rp, branch=0.4, uniform=T); text(nhg3.rp, digits=3) Error in

Dear R-users I am using RPART package to get regression trees. However having trouble getting the text function to put the full splitting rule number on the plot, instead to puts it in scientific notation. When a covariate has 1e4 or greater number of digits then the splitting rule number displayed on the plot is in scientific notation. But print.rpart displays the splitting rules in full.

Hi, I have a question regarding how to get some partial information from the output of rpart, which could be used as the first argument to predict. For example, in my code, I try to learn a stump tree (decision tree of depth 2):    "fit        <- rpart(y~bx, weights = w/mean(w), control = cntrl)     print(fit)     btest[1,]  <- predict(fit, newdata = data.frame(bx)) " I found

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