r-caret

I've trained a tree model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error: Error in confusionMatrix.default(predictionsTree, testdata$catgeory) : the data and reference factors must have the same number of levels prob <- 0.5 #Specify class split singleSplit <- createDataPartition(modellingData2$category, p=prob, times=1, list=FALSE) cvControl <- trainControl(method="repeatedcv", number=10, repeats=5) traindata <- modellingData2[singleSplit,] testdata <- modellingData2[-singleSplit,] treeFit <- train(traindata$category~., data=traindata,

Let me start by saying that I have read many posts on Cross Validation and it seems there is much confusion out there. My understanding of that it is simply this: Perform k-fold Cross Validation i.e. 10 folds to understand the average error across the 10 folds. If acceptable then train the model on the complete data set. I am attempting to build a decision tree using rpart in R and taking advantage of the caret package. Below is the code I am using. # load libraries library(caret) library(rpart) # define training control train_control<- trainControl(method="cv", number=10) # train the model

A similar question was asked however the link in the answer points to random forest example, it doesn't seem to work in my case. Here is an example what I'm trying to do: gbmGrid <- expand.grid(interaction.depth = c(5, 9), n.trees = (1:3)*200, shrinkage = c(0.05, 0.1)) fitControl <- trainControl( method = "cv", number = 3, classProbs = TRUE) gbmFit <- train(strong~.-Id-PlayerName, data = train[1:10000,], method = "gbm", trControl = fitControl, verbose = TRUE, tuneGrid = gbmGrid) gbmFit Everything goes fine, I get the best parameters. Now if I do the prediction: predictStrong = predict(gbmFit,

parRF from the caret R package is not working for me with more than one core, which is quite ironic, given the par in parRF stands for parallel. I'm on a windows machine, if that is a relevant piece of information. I checked that I'm using the latest an greatest regarding caret and doParallel. I made a minimal example and and give the results below. Any ideas? Source code library(caret) library(doParallel) trCtrl <- trainControl( method = "repeatedcv" , number = 2 , repeats = 5 , allowParallel = TRUE ) # WORKS registerDoParallel(1) train(form = Species~., data=iris, trControl = trCtrl, method=

I am using the caret package to train a model with "rpart" package; tr = train(y ~ ., data = trainingDATA, method = "rpart") Data has no missing values or NA's, but when running the command a warning message comes up; Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures. Does anyone know (or could point me to where to find an answer) what does this warning mean? I know it is telling me that there were missing values in resampled performance measures - but what does that exactly mean and how can a

I've build a model using caret. When the training was completed I got the following warning: Warning message: In train.default(x, y, weights = w, ...) : At least one of the class levels are not valid R variables names; This may cause errors if class probabilities are generated because the variables names will be converted to: X0, X1 The names of the variables are: str(train) 'data.frame': 7395 obs. of 30 variables: $ alchemy_category : Factor w/ 13 levels "arts_entertainment",..: 2 8 6 6 11 6 1 6 3 8 ... $ alchemy_category_score : num 3737 2052 4801 3816 3179 ... $ avglinksize : num 2.06 3.68