similar to: LOOCV over SVM,KNN

Hi All, I am trying to use the C50 package to build classification trees in R. Unfortunately there is not enought documentation around its use. Can anyone explain to me - how to prune the decision trees? Regards, Indrajit [[alternative HTML version deleted]]

Hi all, I have a package that is specific to a task I was repetitively using a few years ago. I now needed to run it again with new data. However I am told it was built with an older version or R and will not work. How can I tweak the package so it will run on 11.1? It was a one-off product and has not been maintained. Is there a way to "unpackage" it and repackage it to work? I

Hi, I am using some classifiers in RWeka packages and met a couple problems. (1) J48 implements C45 classifier, the C45 should be able to handle missing values in both training set and test set. But I found the J48 classifier can not be evaluated on test set with missing values--it just ignore them. (2) The ensemble classifiers in RWeka such as bagging and boosting: there is a

Hy, I got the following problem when trying to build a rpart model and using everything but LOOCV. Originally, I wanted to used k-fold partitioning, but every partitioning except LOOCV throws the following warning: ---- Warning message: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method, : There were missing values in resampled performance measures. ----- Below are some

Hello, I would like to test some learning algorithms (C5.0, or C4.5) using decision trees or neural network. I didn't find anything in the R documentation for functions to apply on datasets (well, English is not my mother tongue and I have some problems to understand some functions descriptions). Does anybody know where I can find anything about that ? Thank you very much, Thomas

Hello guys! I am working with some classifiers ( SVM,C4.5,RNA,etc) using 10-C.V. Once I have the model of each one, I make the validation of these models in one dataset. Then,with my model and the dataset, I extract a confusion matrix to know the capacity of prediction from the model. And finally, I extract the accuracy of this prediction based on the diagonal from the confusion matrix. The

Hello, I have the following function that receives a "function pointer" formal parameter name "fnc": loocv <- function(data, fnc) { n <- length(data.x) score <- 0 for (i in 1:n) { x_i <- data.x[-i] y_i <- data.y[-i] yhat <- fnc(x=x_i,y=y_i) score <- score + (y_i - yhat)^2 } score <- score/n

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75

Hello, How can I do a cross validation in R? Thank You!

I am trying to write a wrapper for the last example in help(cv.glm) that deals with leave-one-out-cross-validation (LOOCV) for a logistic model. This wrapper will be used as part of a bigger program. Here is my wrapper funtion : logistic.LOOCV.err <- function( formu=NULL, data=NULL ){ cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 ) glmfit <- glm(

Dear all, not really a R question but: If I want to check for the classification accuracy of a LDA with previous PCA for dimensionality reduction by means of the LOOCV method: Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA with the CV option set to TRUE (runs LOOCV) -- OR-- do I need - to compute for each 'test-bag' (the n-1 observations) a PCA

Hello! Does anyone know how to apply bagging for SVM? ( for example) I am using adabag package to execute bagging but this method, "bagging", works with classification trees. I would like to apply my bagging to other classifiers as SVM,RNA or KNN. Has anyone do it? Thanks!! [[alternative HTML version deleted]]

I am using machine learning for one researching. I am using some classifiers with 5-fold CV . I would like to know how it is possible to extract the accuracy, for example, for KNN,neural networks and J48, for each one of 5-fold because when I apply CV to my classifier, I obtain the "mean accuracy" of 5-fold but each accuracy/error of each fold is not returned. Any help is welcome and

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) &&

Hola, ¿cómo están? Soy estudiante de Ing. en Sistemas de Información y estoy cursando la materia Tecnologías para la Explotación de Datos. Me he decidido a usar R para hacer DM. Estoy recién comenzando con ambos. El asunto es que no he encontrado la implementación en R de uno de los algoritmos que dimos para árboles de decisión, ID3. Ya revisé varios de los paquetes para DM

The question is general for all functions, but here is a specific example - # For the logistic regression of the following correlated variables: C <- c(457, 1371, 4113, 12339, 37017, 111051, 333153, 999459) E <- c(0.003858377, 0.014334578, 0.014092836, 0.737950754, 0.996371828, 0.997482379, 1.005569257, 0.994382856) # The nls function: A = nls(E~(Em*C^p)/(C50^p + C^p),

Greetings all. I just enabled all repos on my system, to enable me to find a particular package. This is the first time I have noticed all the Centos repos < 5.6 being listed. Is this a new feature, to include all repos pre 5.6 now? Here's the output of the aforementioned yum command - sorry it's a little bit long: [root at karsites ~]# yum --enablerepo '*' --disablerepo

Here is my problem, 100 decision trees were built(similar to random forest) and I want to replace some of them by new trees. How can I define a tree array including 100 trees, i.e. t[100], and every t[n] is an "C5.0" object, such that when a new tree comes, i can do n<-10 t[n]<-C5.0(...) -- View this message in context:

hi! this is a question about lda (MASS) in R on a particular dataset. I'm not a specialist about any of this but: First with the well-known "iris" dataset, I tried using lda to discriminate versicolor from the other to classes and I got approx. 70% of accuracy testing on train set. In iris, versicolor stands "between" the 2 other so one can expect lda not to perform well

Hello all. I have this code: myLOOCV <- function(myformula, data) { Y <- all.vars(myformula)[1] Scores<- numeric(length(data[,1])) for (i in 1:length(data[,1])) { train <- data[-i,] test <- data[i,] myModel <- lrm(myformula, train) Scores[i] <- predict(myModel, test,type="mean") }