Agfdhyk

I am running for example the following code:

v.ctrl <- trainControl(method = "repeatedcv", repeats = 1,number = 3, 

                   summaryFunction = twoClassSummary,

                   classProbs = TRUE,

                   allowParallel=T)



xgb.grid <- expand.grid(nrounds = 10000,

                        eta = c(0.01,0.05,0.1),

                        max_depth = c(2,4,6,8,10,14))

set.seed(45)

xgb_tune <-train(target~.,

                 data = train,

                 method = "xgbTree",

                 trControl = cv.ctrl,

                 tuneGrid = xgb.grid,

                 verbose = TRUE,

                 metric = "LogLoss",

                 nthread = 3)

The error is simple:

Error in train(target ~ ., data = train, method = "xgbTree", trControl = cv.ctrl, :
unused arguments (data = train, method = "xgbTree", trControl = cv.ctrl, tuneGrid = xgb.grid, verbose = T, metric = "LogLoss", nthread = 3)

My dataset

structure(list(feature19 = c(0.58776, 0.40764, 0.4708, 0.67577, 0.41681, 0.5291, 0.33197, 0.24138, 0.49776, 0.58293), feature6 = c(0.48424, 0.48828, 0.58975, 0.33185, 0.6917, 0.53813, 0.76235, 0.7036,     0.33871, 0.51928), feature10 = c(0.61347, 0.65801, 0.69926, 0.23311,     0.8134, 0.55321, 0.72926, 0.663, 0.49206, 0.55531), feature20 = c(0.39615,     0.49085, 0.50274, 0.6038, 0.37487, 0.53582, 0.62004, 0.63819,     0.37858, 0.40478), feature7 = c(0.55901, 0.38715, 0.50705, 0.76004,     0.3207, 0.54697, 0.31014, 0.21932, 0.4831, 0.52253), feature4 = c(0.5379,     0.52526, 0.44264, 0.28974, 0.65142, 0.41382, 0.44205, 0.47272,     0.6303, 0.56405), feature16 = c(0.41849, 0.45628, 0.37617, 0.39334, 0.46727, 0.36297, 0.3054, 0.41256, 0.6302, 0.41892), feature2 = c(0.62194,  0.5555, 0.61301, 0.27452, 0.74148, 0.49785, 0.5215, 0.46492,     0.54834, 0.58106), feature21 = c(0.32122, 0.37679, 0.35889, 0.74368,     0.18306, 0.47027, 0.40567, 0.47801, 0.41617, 0.35244), feature12 = c(0.56532,     0.55707, 0.49138, 0.24911, 0.69341, 0.42176, 0.41445, 0.45535,     0.62379, 0.5523), target = c(1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,     1L, 1L)), .Names = c("feature19", "feature6", "feature10", "feature20",     "feature7", "feature4", "feature16", "feature2", "feature21",     "feature12", "target"), row.names = c(NA, 10L), class = "data.frame")

Does anyone know whether I have to reprocess the data for xgbtree?
Thx u!

I realize I am kind of a noob when it comes to R/Caret/machine learning, but I saw your post after constantly checking for responses to my question and I managed to get your code working. I hope someone more knowledgeable is able to fully answer your questions, but in the meantime, here is what I did.

First, I inputted your data set into R and tried running your code. I believe you may have a typo in your control function, you are missing a "c" in "cv" which may lead to the issues you are having with unused arguments.

However, after I resolved that issue there were multiple errors and warnings; for one, you are using twoClassSummary but specifying logLoss (note the syntax here, it's not LogLoss in case that changes anything)...instead I switched this summaryFunction to mnlog to call the logLoss function properly, as from what I've read twoClassSummary uses AUC as its metric. Also, I replaced your "target" variable in your training set with a simple character variable, in this case, "Y" or "N". You can download the csv file here.

After, I kept receiving an error regarding your tuning grid, stating that essentially your were missing tuning parameters for xgBoost methods which can be found in the documentation for caret (available models). I simply added the default values for the rest of the parameters (most of which were 1). The tuning grid I used can be found here.

My final code I used to actually train the xgb model was as follows:

control = trainControl(method = "repeatedcv", repeats = 1, number = 3, 

                   summaryFunction = mnLogLoss,

                   classProbs = TRUE,

                   allowParallel=T)



tune = train(x=set[,1:10], y=set[,11], method="xgbTree", trControl=control, 

tuneGrid = xgb.grid, verbose=TRUE, metric="logLoss", nthread=3)

And the output is shown here:

tune

eXtreme Gradient Boosting 



10 samples

10 predictors

2 classes: 'N', 'Y' 



No pre-processing

Resampling: Cross-Validated (3 fold, repeated 1 times) 

Summary of sample sizes: 6, 8, 6 

Resampling results across tuning parameters:



  eta   max_depth  logLoss  

  0.01   2         0.6914816

  0.01   4         0.6914816

  0.01   6         0.6914816

  0.01   8         0.6914816

  0.01  10         0.6914816

  0.01  14         0.6914816

  0.05   2         0.6848399

  0.05   4         0.6848399

  0.05   6         0.6848399

  0.05   8         0.6848399

  0.05  10         0.6848399

  0.05  14         0.6848399

  0.10   2         0.6765847

  0.10   4         0.6765847

  0.10   6         0.6765847

  0.10   8         0.6765847

  0.10  10         0.6765847

  0.10  14         0.6765847



Tuning parameter 'nrounds' was held constant at a value of 10000

Tuning parameter 'gamma' was held constant at a

 value of 0

Tuning parameter 'colsample_bytree' was held constant at a value of 1

Tuning parameter

 'min_child_weight' was held constant at a value of 1

Tuning parameter 'subsample' was held constant at a value of 1

logLoss was used to select the optimal model using  the smallest value.

The final values used for the model were nrounds = 10000, max_depth = 2, eta 

= 0.1, gamma = 0, colsample_bytree =

 1, min_child_weight = 1 and subsample = 1.

I hope this helps, and was what you were seeking. I am a bit suspicious if I did the log loss command correctly because it would appear max depth literally had no effect on log loss. I reran the model using a different metric, AUC, and the results showed no effect regardless of what was changed, and same with Cohen's Kappa. I'm guessing this is due to only ten samples, but hopefully somebody can actually explain what I did so this is more than just a code dump.