search for: trainingdata

...omeone has a suggestion, please advise. The goal of the network is to properly classify a number as positive or negative. Simple 1-layer network with a single neuron in each layer. Rcode: trainInput <- matrix(rnorm(10)) trainAnswers <- ifelse(trainInput <0, -1, 1) trainNeurons <- 1 trainingData <- mlptrain(inp=trainInput, neurons=trainNeurons, out=trainAnswers, it=1000) ## To call this network, we can see how it works on a set of known positive and negative values testInput <- matrix(-2:2) mlp(testInput, trainingData$weight, trainingData$dist, trainingData$neurons, trainingData$a...

...that I've split into two halves, one for training the logistic model, and the other for evaluating it. The structure is as follows: column headers are "got a loan" (dichotomous), "hourly income" (continuous), and "owns own home" (dichotomous) The training data is *trainingData[1,] = c(0,12,0)* *...* etc and the validation data is *validationData[1,] = c(1,35,1)* *...* etc I use Prof. Harrell's excellent Design modules to perform a logistic regression on the training data like so: *logit.lrm <- lrm(gotALoan ~ hourlyIncome+ownsHome, data=trainingData)* *lrm(formul...

with pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs] dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2))); results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc", C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T) and a degree of...

...23 4 7 type2 30 ..... 18 11 type1 45 if I train a PCR model using the training data #2 and try to predict with the resulting model and the data from "newdata.csv", e.g., ################################## trainingdata <- read.csv("mydata_without_train_column.csv", header=TRUE) trainingdata <- data.frame(trainingdata) testingdata <- read.csv("newdata.csv", header=TRUE) testingdata <- data.frame(testingdata) pcrmodel2 <- pcr(response ~ var1+var2+var3, data = trainingdata) pr...

Just curious if anyone else has got this error before, and if so, would know what I could do (if anything) to get past it: > mtry <- tuneRF(training, trainingdata$class, ntreeTry = 500, stepFactor = 2, improve = 0.05, trace = TRUE, plot = TRUE, doBest = FALSE) mtry = 13 OOB error = 0.62% Searching left ... mtry = 7 OOB error = 1.38% -1.222222 0.05 Searching right ... mtry = 26 OOB error = 0.24% 0.6111111 0.05 mtry = 52 OOB error = 0.07% 0...

...quot;, returnResamp = "all", returnData=TRUE, verboseIter = TRUE) rftrain <- train(x=train_x, y=trainclass, method="rf", tuneGrid=tuneGrid, tr.control=rfControl) pred <- predict(rftrain) pred # this works fine expred <- extractPrediction(rftrain) Error in models[[1]]$trainingData : $ operator is invalid for atomic vectors My predictors are 28 numeric attributes and one factor. I`m working with the latest version of caret and R 2.7.2 on WinXP. Any advice is very welcome. Thanks. TIM ------------------------------------------------------------------------------- Dipl...

...ppears in the data there are a lot of fields that are constant or all missing values - which prevents the model from being built. Can someone point me the right direction as to how I can automatically purge my data file of these useless fields. Thanks in advance, pdb train <- read.csv("TrainingData.csv") library(gbm) i.gbm<-gbm(TargetVariable ~ . ,data=train,distribution="bernoulli..... 1: In gbm.fit(x, y, offset = offset, distribution = distribution, ... : variable 5: var1 has no variation. -- View this message in context: http://r.789695.n4.nabble.com/eliminating-constant-...

Dear all, I have a variable that I would like also to use it as a string. The reasons is that I want to collect results from different function to one table.. So when I use the  colnames(mymatrix) <-c(function1.function2,function3) the function1, function2, function3 to be "converted" to simple strings so as  colnames(mymatrix)

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);