similar to: RWeka cross-validation and Weka_control Parametrization

Hello, I have some trouble in achieving the desired parametrisation for the weka classifier functions, using the package RWeka. The problem is, that the functions result=classifier(formula, data, subset, na.action, control = Weka_control(mycontrol)) do not seem to be manipulated by the mycontrol- arguments Perhaps this should be resepected via the handlers- argument , but the

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Dear All, I am finding trouble trying to guild a Wrapper using random forest to evaluate the subsets: I do: nombi <- make_Weka_filter("weka/filters/supervised/attribute/AttributeSelection") datbin<- nombi(gene ~., data=X1X2X4X5W, control =Weka_control( S=list("weka.attributeSelection.GeneticSearch"), E=list("weka.attributeSelection.WrapperSubsetEval"),B

A couple of my functions that were working last week seem to have been changed over the weekend and no longer work, but I can't understand why not: it seems that objects defined at the start of the function are not located further on in the function, when this worked fine before. An example follows at the end, using the package spatstat, though the problem seems more general. My only guess is

hello , I am new user in R . I have datafile (class = data.frame) which has 825 columns with unique column name i want extract 200 selected column from datafile how can I do this? my datafile look like.. Mi RBN RBF nDB nX 3 2.6225979 0.53132756 -0.80599902 -1.4471864 -0.5705269 10 0.4818746 -1.72143092 -2.19579027 2.0118824 -0.5705269 12 2.8519611

Hi! If i found a problem with the code of one of the libraries (not core), or, in my current case, would wish something minor changed for convenience, where can i get contact? Can i put it in the "official" bug repository? (Problem discription for anyone interested: Why call the default function kpca for a matrix with kpar=list(sigma=0.2), instead of putting this default sigma into the

Hi all, I'm having this error, since I'm working with a data matrix I don't understand what's happening; I've tried several ways to solve this, even working with sparse matrix, but nothing seems to solve it, I've also tried svm (with a simple matrix 3*3 and still got the same error. > dados<-read.table("b.txt",sep="",nrows=30000) >

Dear List, I´m trying to implement the functionalities from WEKA into my modeling project in R through the RWeka package. In this context I have a slightly special question about the filters implemented in WEKA. I want to convert nominal attributes with k values into k binary attributes through the NominalToBinary filter ("weka.filters.supervised.attribute.NominalToBinary"). But

Hi all, sorry for my english, but I don't speak yours language. I'm trying to use bart() and rbf(). The package I'm using now is "BayesTree" and "neural", respectively. I could create the models, but I can't predict my test data. Does anyone have such an experience? Any advice is appreciated! Thank you in advanced!. Andr? -- View this message in

On 06/28/2016 10:53 AM, Roman Gareev wrote: > 2016-06-27 15:52 GMT+05:00 4lbert C0hen <4lbert.h.c0hen at gmail.com>: >> Dear Roman and all, >> >> Such features would be extremely useful to implement array expansion (scalar >> and array renaming, privatization with new subscript expressions of higher >> dimension) and storage mapping optimization (generalizing

Hi!! I am trying to figure out how to use the string kernel "stringdot" in kernlab. k <- function(x,y) { (sum(x*y) +1)*exp(-0.001*sum((x-y)^2)) } class(k) <- "kernel" data(promotergene) ## train svm using custom kernel gene.k <- ksvm(Class~.,data=promotergene,kernel=k,C=10,cross=5) # works fine in this case gene.rbf <-

I'm a big fan of both Weka and R (quite new at R :) ), and jumped at the chance to use them together. Unfortunately, I'm running into what is probably a dumb error when trying to view info about my model. A Google search turned up 0 hits for the actual error I got (last line), but you all are smarter! My code is below, but basically my data frame (q) is imported via RODBC and has 1586

Dear community, I have a problem with a string conversion: > text [1] "" "and" "\xc1d\xe1m" [4] "graphical" "interface" "MLP" [7] "Nagy" "networks" "Networks" [10] "neural" "Neural"

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or

Hi, I have a question about using RWeka Clusterers.If you could supply answer or insight, I would really appreciate it. When I run a simple code which uses a clusterer from RWeka I get an error. the sample codes and errors are mentioned below Code: library(RWeka) Cobweb(iris[,-5],control=NULL) Error: Error in names(class_ids) <- nms : 'names' attribute [150] must be the same

Is there a method to list the components of an object, instead of looking at the help for that method? Let me be more clear with an example data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find

Hi, If I do a standard svm regression with e1071 x <- seq(0.1, 5, by = 0.05) y <- log(x) + rnorm(x, sd = 0.2) m <- svm(x, y) we can do predict(m,x) to get the fitted values. But what if I wan tho get them by hand? Seem to me like it should be w = t(m$coefs)%*%m$SV x.scaled = scale(x, m$x.scale[[1]], m$x.scale[[2]]) t(w %*% t(as.matrix(x.scaled))) - m$rho but this is wrong If i

dear: when I read a Excel file(exp-11),The R project give me a error ,Just like this: ??'datafile' > write.foreign("exp-11.xls") ???list(df = df, datafile = datafile, codefile = codefile) : ??'datafile' > write.foreign("exp-11.xls") ???list(df = df, datafile = datafile, codefile = codefile) : ??'datafile' >