I am using the following code to tune the 4 parameters of Gradient Boosting algorithm using Simulated annealing (optim). When I run the program, after few seconds it stops and displays the following error:
I point out here that the same code works for RF ( mtry parameter) and SVM (cost and sigma parameters). So, I guess the problem should be in the 4 parameters of GBM Something is wrong; all the MAE metric values are missing: RMSE Rsquared MAE Min. : NA Min. : NA Min. : NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA Median : NA Median : NA Median : NA Mean :NaN Mean :NaN Mean :NaN 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA Max. : NA Max. : NA Max. : NA NA's :1 NA's :1 NA's :1 Code is here/// If you need the dataset, I can attach in the email d=readARFF("dat.arff") ///DATA IS REGRESSION BASED index <- createDataPartition(log10(d$Price), p = .70,list = FALSE) tr <- d[index, ] ts <- d[-index, ] index_2 <- createFolds(log10(tr$Price), returnTrain = TRUE, list = TRUE) ctrl <- trainControl(method = "cv", index = index_2) obj <- function(param, maximize = FALSE) { mod <- train(log10(Price) ~ ., data = tr, method = "gbm", preProc = c("center", "scale", "zv"), metric = "MAE", trControl = ctrl, //HERE IN tuneGrid WHEN I USE PARAMETERS FOR SVM AND RF, IT WORKS, BUT FOR GBM, IT DOES NOT WORK tuneGrid = data.frame(n.trees = 10^(param[1]), interaction.depth = 10^(param[2]), shrinkage=10^(param[3]), n.minobsinnode=10^(param[4]))) if(maximize) -getTrainPerf(mod)[, "TrainMAE"] else getTrainPerf(mod)[, "TrainMAE"] } num_mods <- 50 ## Simulated annealing from base R /// I JUST USED HERE SOME INITIAL POINTS OF THE 4 PARAMETERS OF GBM san_res <- optim(par = c(10,1,0.1,1), fn = obj, method = "SANN", control = list(maxit = num_mods)) san_res [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.