r-caret

I am very new at machine learning and am attempting the forest cover prediction competition on Kaggle , but I am getting hung up pretty early on. I get the following error when I run the code below. Error in train.default(x, y, weights = w, ...) : final tuning parameters could not be determined In addition: There were 50 or more warnings (use warnings() to see the first 50) # Load the libraries library(ggplot2); library(caret); library(AppliedPredictiveModeling) library(pROC) library(Amelia) set.seed(1234) # Load the forest cover dataset from the csv file rawdata <- read.csv("train.csv"

I'm using the caret function "train()" in one of my project and I'd like to add a "custom metric" F1-score. I looked at this url caret package But I cannot understand how I can build this score with the parameter available. There is an example of custom metric which is the following: ## Example with a custom metric madSummary <- function (data, lev = NULL, model = NULL) { out <- mad(data$obs - data$pred, na.rm = TRUE) names(out) <- "MAD" out } robustControl <- trainControl(summaryFunction = madSummary) marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2) earthFit <- train(medv ~ ., data =

When I train just using glm , everything works, and I don't even come close to exhausting memory. But when I run train(..., method='glm') , I run out of memory. Is this because train is storing a lot of data for each iteration of the cross-validation (or whatever the trControl procedure is)? I'm looking at trainControl and I can't find how to prevent this...any hints? I only care about the performance summary and maybe the predicted responses. (I know it's not related to storing data from each iteration of the parameter-tuning grid search because there's no grid for glm's, I believe.) The

Using the R package caret, how can I generate a ROC curve based on the cross-validation results of the train() function? Say, I do the following: data(Sonar) ctrl <- trainControl(method="cv", summaryFunction=twoClassSummary, classProbs=T) rfFit <- train(Class ~ ., data=Sonar, method="rf", preProc=c("center", "scale"), trControl=ctrl) The training function goes over a range of mtry parameter and calculates the ROC AUC. I would like to see the associated ROC curve -- how do I do that? Note: if the method used for sampling is LOOCV, then rfFit will contain a non-null data frame in the rfFit$pred

I recently started to look into caret package for a model I'm developing. I'm using the latest version. As the first step, I decided to use it for feature selection. The data I'm using has about 760 features and 10k observations. I created a simple function based on the training material on line. Unfortunately, I consistently get an error and so the process never finishes. Here is the code that produces error. In this example I am using a small subset of features. I started with the full set of features. I've also changed the subsets, number of folds and repeats to no avail. I know it will be