r-caret

问题 When calling the train function of the caret package, the data is automatically transformed so that all factor variables are turned into a set of dummy variables. How can I prevent this behaviour? Is it possible to say to caret "don't transform factors into dummy variables"? For example: If I run the rpart algorithm on the etitanic data: library(caret) library(earth) data(etitanic) etitanic$survived[etitanic$survived==1] <- 'YES' etitanic$survived[etitanic$survived!='YES'] <- 'NO' model<

问题 I'm getting the following error when I try to run createDataPartition in caret. Error in createDataPartition(data1, p = 0.8, list = FALSE) : y must have at least 2 data points I ran the same exact same code last night with no errors. Any thoughts? predictors<- with(df, data.frame(xvar, xvar, xvar, xvar)) data1<-with(dfu2, data.frame(data1)) library(caret) set.seed(1) trainingRows<- createDataPartition(data1, p=.80, list=FALSE) > dput(head(data1, 15)) structure(list(data1 = c(1L, 1L, 1L, 1L,

问题 i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the

问题 How long does it generally take to install caret package in R? I ran install.packages('caret', dependencies = TRUE), and R has been running the install for close to an hour now. Is this normal? 回答1: It shouldn't take that long, I actually had to install the Caret package on one of my machines earlier today and it took less than a minute. Sounds like you might want to check your connection speed. 回答2: With dependencies, it can take a while, mine took about 30mins and my download speed was

问题 I'm having the same issue as here, but the solutions are not working for me. I'm not sure what I'm doing wrong... Here is my code: # ensure results are repeatable set.seed(7) # load the library library(caret) # load the dataset dataset <- read.csv("C:\\Users\\asholmes\\Documents\\diabetes_r.csv", na.strings='.') #convert to data frame as.data.frame(dataset, stringsAsFactors=TRUE) #create x and y x <- dataset[, 1:15] y <- dataset[, 16] # prepare training scheme control <- trainControl(method=

问题 I took the following code out of a textbook, "Machine Learning With R" by Brett Lantz, however copied exactly the same to the console from the textbook, > library(caret) Loading required package: lattice Loading required package: ggplot2 > library(kernlab) Attaching package: ‘kernlab’ The following object is masked from ‘package:ggplot2’: alpha > set.seed(300) > ctrl <- trainControl(method = "cv", number = 10) > bagctrl <- bagControl(fit = svmBag$fit, predict = svmBag$pred, aggregate = svmBag

问题 I wonder it is possible to use random search in a predefined grid. For example, my grid has alpha and lambda for glmnet method. alpha is between 0 and 1, and lambda is between -10 to 10. I want to use random search 5 times to randomly try points in this bound. I wrote the following code for grid search and it works fine, but I cannot modify it for random search in a bound. rand_ctrl <- trainControl(method = "repeatedcv", repeats = 5, search = "random") grid <- expand.grid(alpha=seq(0,1,0.1)

问题 I am training a bagFDA model using train() function in r caret package, and save the model output as a .Rdata file. the input file is about 300k records with 26 variables, but the output .Rdata has a size of 3G. I simply run the following: modelout <- train(x,y,method="bagFDA") save(file= "myout.Rdata", modelout) under a window system. question: (1) why myout.Rdata is so big? (2) how can I reduce the size of the file? Thanks in advance! JT 回答1: In the trainControl set returnData = FALSE for

问题 I am an actuarial student preparing for an upcoming predictive analytics exam in December. Part of an exercise is to build a model using boosting with caret and xgbTree. See the code below, the caravan dataset is from the ISLR package: library(caret) library(ggplot2) set.seed(1000) data.Caravan <- read.csv(file = "Caravan.csv") data.Caravan$Purchase <- factor(data.Caravan$Purchase) levels(data.Caravan$Purchase) <- c("No", "Yes") data.Caravan.train <- data.Caravan[1:1000, ] data.Caravan.test <

问题 This question builds on the question that I asked here: Creating data partitions over a selected range of data to be fed into caret::train function for cross-validation). The data I am working with looks like this: df <- data.frame(Effect = rep(seq(from = 0.05, to = 1, by = 0.05), each = 5), Time = rep(c(1:20,1:20), each = 5), Replicate = c(1:5)) Essentially what I would like to do is create custom partitions, like those generated by the caret::groupKFold function but for these folds to be