r-caret

问题 I have a problem with specifying the learning rate using the caret package with the method "mlpWeightDecay" from RSNNS package. The tuning parameters of "mlpWeightDecay" are size and decay. An example leaving size constant at 4 and tuning decay over c(0,0.0001, 0.001, 0.002): data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] fit1 <- train(TrainData, TrainClasses, method = "mlpWeightDecay", preProcess = c("center", "scale"), tuneGrid=expand.grid(.size = 4, .decay = c(0,0.0001, 0.001,

问题 I would like to know if there is a way to plot the average ROC Curve from the cross-validation data of a SVM-RFE model generated with the caret package. My results are: Recursive feature selection Outer resampling method: Cross-Validated (10 fold, repeated 5 times) Resampling performance over subset size: Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD Selected 1 0.6911 0.0000 1.0000 0.5900 0.0000 0.2186 0.0000 0.0000 0.0303 0.0000 2 0.7600 0.3700 0.8067 0.6280 0

问题 i am using the caret package. At specific instances, as for example, but not exclusively per the example below, caret will produce the following error: Something is wrong; all the ROC metric values are missing: Error in train.default(x, y, weights = w, ...) : Stopping The strange thing is that caret might do this with nearly identical calls. E.g. - the first call per below works, the second call gives an error. i have had similar issue when i included e.g. the Verbose argument, keeping all

问题 I have an imbalanced data set with two classes therefore I thought I could use ROC as a metric instead of Accuracy to tune my model in R using caret package (I am trying different methods such as rpart, rf..etc). I thought we could extract probabilities and use ROC as a metric in decision tree type algorithms as well using caret. I illustrate my problem using a data set in caret below. There are three classes in this data but I redefined and created two classes for illustration purposes. I

问题 From the documentation: For bootstrap samples, simple random sampling is used. For other data splitting, the random sampling is done within the levels of y when y is a factor in an attempt to balance the class distributions within the splits. For numeric y, the sample is split into groups sections based on percentiles and sampling is done within these subgroups. For createDataPartition, the number of percentiles is set via the groups argument. I don't understand why this "balance" thing is

问题 I am running elastic net regularization in caret using glmnet . I pass sequence of values to trainControl for alpha and lambda, then I perform repeatedcv to get the optimal tunings of alpha and lambda. Here is an example where the optimal tunings for alpha and lambda are 0.7 and 0.5 respectively: age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7, 6, 8, 11, 11, 6, 2, 10, 14, 7, 12, 6, 9, 10, 14, 7) gender <- make.names(as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1)))

问题 I used caret for logistic regression in R: ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10, savePredictions = TRUE) mod_fit <- train(Y ~ ., data=df, method="glm", family="binomial", trControl = ctrl) print(mod_fit) The default metric printed is accuracy and Cohen kappa. I want to extract the matching metrics like sensitivity, specificity, positive predictive value etc. but I cannot find an easy way to do it. The final model is provided but it is trained on all the data

问题 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 =

问题 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 =

问题 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