similar to: Aggregate function Bagging

Hi I'm trying to predict using a model I fitted with SVM. I constructed the model (called Svm) using a training set, and now I want to use a test set (called BankTest) for prediction. The response variable is in the first column of BankTest. > SvmPred = predict(Svm, BankTest[,-1], probability=TRUE) > SvmPredRes = table(Pred = SvmPred, True = BankTest[,1]) I get this error: Error in

Hola, El mensaje es claro: el modelo svmfit no existe, tú has llamado al ajuste ?modelo?. De todas formas, aparte de eso tendrías que especificar qué dimensiones (variables predictivas) quieres representar. Si miras en la ayuda de ?plot.svm lo tienes explicado. Esto sí funcionaría: plot(modelo,df11, LDH ~ INL ) Gracias por proporcionar el código y los datos para poder reproducir el error. Un

Estimados En este modelo no puedo hacer el plot(svmfit,df11 ) #AQUI NO TRABAJA Le adjunto Excel library(readxl) df11 attach(df11) df11$fallecido=factor(df11$fallecido) # Selección de una submuestra del 70% de los datos set.seed(101) tamano.total <- nrow(df11) tamano.entreno <- round(tamano.total*0.7) datos.indices <- sample(1:tamano.total , size=tamano.entreno) datos.entreno <-

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]]

Hi, Is here anyone knows the difference between rapart and bagging when grow a CART tree? Thanks Qin

Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error

> library(ROCR) > library(e1071) svmres.prob <- svm(traindx, traindy, probability=TRUE) svmpred.prob <- predict(svmres.prob, testdx, probability = TRUE, decision.values = TRUE, type="prob") > print(length(attr(svmpred.prob, "probabilities"))) [1] 0 > print(attr(svmpred.prob, "probabilities")) NULL > print(attributes(svmpred.prob)$decision.values)

Hi there. I was wondering if somebody knows how to perform a bagging procedure on a classification tree without running the classifier with weights. Let me first explain why I need this and then give some details of what I have found out so far. I am thinking about implementing the bagging procedure in Matlab. Matlab has a simple classification tree function (in their Statistics toolbox) but

Dear sir, If I want to use bagging with SVM, which package should I choose?Thanks! Best wishes,Jie _________________________________________________________________ [[alternative HTML version deleted]]

Dear sir,I have a data set which name is "c78p",now I want to deal with it with bagging. % of data from c78p as training set is 50%,and number of bootstrap random samples with replacement from c78p is 5,use SVM in each run,can you help me to write the code?Thanks.Best regards,Jie _________________________________________________________________ [[alternative HTML version

I am using bagging to perform Bagged Regression Trees on count data (bird abundance in Britain and Ireland, in relation to climate and land cover variables). Predictions from the final model are visually believable but I would really like a diagnostic equivalent to classification success that can be used to decide if a model is adequate. Whereas with classification data an error rate is returned,

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

Dear Sir, I am a R beginning user. I would like to apply the bagging algorithm to my data in order to classify a certain disease by Bagging Support Vector Machine Ensemble. My problem is that even if I am reading the book, looking at the examples in internet and available in R, learning a lot of theoretical things on about SVM esemble or Bagging method, I can't apply Bagged SVM esemble or

I'm having the most weird problem with bagging function. For some unknown reason it does not improve the classification (compared to rpart), but instead gives much worse results ! Running rpart on my data gives error rate of about 0.3 and bagging, instead of improving this results, gives error rate of 0.9 !!! I'm running both rpart and bagging with exactly the same parameters, I even

I wanted to report this issue here so others may not find themselves alone and as the author is apparently active on the list. I havent done an exhaustive test by any means, cause I dont have time. But here's a small example. Apparently the "ns" argument is the one that is killing it. I've gotten several different segfault messages, the only other one I remember said "out

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

I am trying to implement a simple r-svm example using the iris data (only two of the classes are taken and data is within the code). I am running into some errors. I am not an expert on svm's. If any one has used it, I would appreciate their help. I am appending the code below. Thanks../Murli ####################################################### ### R-code for R-SVM ### use leave-one-out

I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion? Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user.

Dear Sir, I got a problem with my program. I would like to classify my data using bagging support vector machine ensemble. I split my data into training data and test data. For a given data sets TR(X), K replicated training data sets are first randomly generated by bootstrapping technique with replacement. Next, Support Vector Mechine (SVM) is applied for each bootstrap data sets. Finally, the

Hello the code : set.seed(10) y<-c(1:1000) x1<-c(1:1000)*runif(1000,min=0,max=2) x2<-c(1:1000)*runif(1000,min=0,max=2) x3<-c(1:1000)*runif(1000,min=0,max=2) lm_fit<-lm(y~x1+x2+x3) summary(lm_fit) set.seed(10) all_data<-data.frame(y,x1,x2,x3) positions <- sample(nrow(all_data),size=floor((nrow(all_data)/4)*3)) training<- all_data[positions,] testing<-