similar to: multi-class classification using rpart

You could break your 3 class problem into several (2 or 3) 2 class problems, and then use Andy's suggestion (see the CART book). There are several ways to break the problem into 2 class problems, and several ways to combine the resulting classifiers. Tom Dietterich, Jerry Friedman, Trevor Hastie and Rob Tibshirani, among others, have articles on the question, in places like Annals of

Hi, there: I am working on a classification problem by using rpart. when my response variable y is binary, the trees grow very fast, but if I add one more case to y, that is making y has 3 cases, the tree growing cannot be finished. the command looks like: x<-rpart(r0$V142~.,data=r0[,1:141], parms=list(split='gini'), cp=0.01) changing cp or removing parms does not help.

Hello, I am trying to select the following headers from a data frame but when I try and run the command it executes halfway through and give me an error at V188 and V359. Temp <- data.frame(V4, V5, V6, V7, V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V40, V41, V42, V43, V44, V45,

Hi. I have a simple (I think) question My dataset have these variables: names(data) [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"

Hi, there: I made a function to do k-fold cross-validation as below. Basically whenever I call cv(test) for example, an error message like: 20Fold 1 Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ please help. My test dataset has 142 variables, the last one is a categorical response variable. also, i am not sure how to save

Hi all, Can anyone who is familar with CART tell me what I missed in my tree code? library (MASS) myfit <- tree (y ~ x1 + x2 + x3 + x4 ) # tree.screens () # useless plot(myfit); text (myfit, all= TRUE, cex=0.5, pretty=0) # tile.tree (myfit, fgl$type) # useless # close.screen (all= TRUE) # useless My current tree plot resulted from above code shows as:

Hi, When I look at the summary of an rpart object run on my data, I get 7 nodes but when I plot the rpart object, I get only 3 nodes. Should the number of nodes not match in the results of the 2 functions (summary and plot) or it is not always the same? Look forward to your reply, Carol -------------------------------------------- ?summary(rpart.res) Call: rpart(formula = mydata$class ~ ., data

Using the data set fgl in MASS the following code layout(matrix(1:9,3,3)) for(i in 1:9){ boxplot(fgl[,i] ~ type, data = fgl,main=dimnames(fgl)[[2]][i])} produces a 3 by 3 array of plots, each one of which consists of six boxplots. Is it possible to do this in lattice? Steve "R version 2.7.2 (2008-08-25)" on Ubuntu 6.06

Dear sir or ma'am, I have a question about the dataset "fgl." The dataset seems to be in the "VR" package, so I tried to download it from CRAN. However, after downloading, when I tried to load the package, it was not in my package list. I am wondering what is wrong. Any advice on how to access the fgl dataset would be appreciated. Thanks. Seungho Huh

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

On a PMac 7200 (running LinuxPPC 3Q 99), trying to install "R-ctest-0.9.R16-1.ppc.rpm" and "R-splines-2.0.R3-3.ppc.rpm" rpms currently in the linuxppc binary download directory results in error messages saying that files in these rpms conflict with files from R-base 1.01-1. Of course, "R-rpart" and "R-survival4" then also can't be installed because the

On 12/9/2016 11:03 AM, Jim Lewis via llvm-dev wrote: > > Thanks, that helps enormously! The issue is that the match is supposed > to support both cascade and tree OR patterns, but there appears to be > a problem with the tree matching. Both test1 and test6 in the ARM > tests exercise the cascade pattern, and I remember now our fix is > confined to the tree case. > > I

Here is a wiki page and git repo with an implementation of SEH that passes all the tests for x86. We're looking for feedback before putting a patch on Phabricator. https://github.com/tentzen/llvm-project https://github.com/tentzen/llvm-project/wiki On Mon, Nov 18, 2019 at 10:59 PM Gaier, Bjoern <Bjoern.Gaier at horiba.com> wrote: > Thank you for this Aaron! > > > > In

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

At Microsoft we open sourced some of our compiler tests for exception handling on github. https://github.com/microsoft/compiler-tests We are looking into what it would take to get these to work with clang-cl. I’m planning to share a design document on the mailing list soon. Aaron -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi, I would like to step-through a non-visible function. but apparently I don't know enough about namespaces to get that to work: > methods(predict) ... deleted lines ... [27] predict.rpart* predict.smooth.spline* [31] predict.survreg.penal* Non-visible functions are asterisked > debug(predict.rpart) Error: Object "predict.rpart" not found >

Hello there. I have fitted a rpart model. > rpartModel <- rpart(y~., data=data.frame(y=y,x=x),method="class", ....) and can use rpart$where to find out the terminal nodes that each observations belongs. Now, I have a set of new data and used predict.rpart which seems to give only the predicted value with no information similar to rpart$where. May I know how

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 am currently studying Decision Trees by using rpart package in R. I artificially created a data set which includes the dependant variable (y) and a few independent variables (x1, x2...). The dependant variable y only comprises 0 and 1. 90% of y are 1 and 10% of y are 0. When I apply rpart to it, there is no splitting at all. I am wondering whether this is because of the

Hello, I just upgraded my Ubuntu Xenial system to R 3.5.1 (from 3.4.?) by changing the sources.list entry and doing an "apt-get dist-upgrade". Everything works except loading the rpart package in R: > library(rpart) Error: package or namespace load failed for ?rpart?: package ?rpart? was installed by an R version with different internals; it needs to be reinstalled for use with