Using adaboost within R's caret package

Question

I've been using the ada R package for a while, and more recently, caret. According to the documentation, caret's train() function should have an option that uses ada. But, caret is puking at me when I use the same syntax that sits within my ada() call.

Here's a demonstration, using the wine sample data set.

library(doSNOW)
registerDoSNOW(makeCluster(2, type = "SOCK"))
library(caret)
library(ada)

wine = read.csv("http://www.nd.edu/~mclark19/learn/data/goodwine.csv")


set.seed(1234) #so that the indices will be the same when re-run
trainIndices = createDataPartition(wine$good, p = 0.8, list = F)
wanted = !colnames(wine) %in% c("free.sulfur.dioxide", "density", "quality",
                            "color", "white")

wine_train = wine[trainIndices, wanted]
wine_test = wine[-trainIndices, wanted]
cv_opts = trainControl(method="cv", number=10)


 ###now, the example that works using ada() 

 results_ada <- ada(good ~ ., data=wine_train, control=rpart.control
 (maxdepth=30, cp=0.010000, minsplit=20, xval=10), iter=500)

##this works, and gives me a confusion matrix.

results_ada
     ada(good ~ ., data = wine_train, control = rpart.control(maxdepth = 30, 
     cp = 0.01, minsplit = 20, xval = 10), iter = 500)
     Loss: exponential Method: discrete   Iteration: 500 
      Final Confusion Matrix for Data:
      Final Prediction
      etc. etc. etc. etc.

##Now, the calls that don't work. 

results_ada = train(good~., data=wine_train, method="ada",
control=rpart.control(maxdepth=30, cp=0.010000, minsplit=20, 
xval=10), iter=500)
   Error in train.default(x, y, weights = w, ...) : 
   final tuning parameters could not be determined
   In addition: Warning messages:
   1: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method,  :
    There were missing values in resampled performance measures.
   2: In train.default(x, y, weights = w, ...) :
    missing values found in aggregated results

 ###this doesn't work, either

results_ada = train(good~., data=wine_train, method="ada", trControl=cv_opts,
maxdepth=10, nu=0.1, iter=50)

  Error in train.default(x, y, weights = w, ...) : 
  final tuning parameters could not be determined
  In addition: Warning messages:
  1: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method,  :
    There were missing values in resampled performance measures.
  2: In train.default(x, y, weights = w, ...) :
   missing values found in aggregated results

I'm guessing it's that train() wants additional input, but the warning thrown doesn't give me any hints on what's missing. Additionally, I could be missing a dependency, but there's no hint on what should be there....

Answer 1

Look up ?train and search for ada you'll see that:

Method Value: ada from package ada with tuning parameters: iter, maxdepth, nu (classification only)

So you must be missing the nu parameter, and the maxdepth parameter.