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David.
On 12/22/19 9:15 AM, Neha gupta wrote:
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
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