How to resolve error while predicting using xgboost in R

@hackers wrote:

Hello,

I am trying to implement xgboost in R for a classification problem:

# xgboost parameters
param <- list("objective" = "binary:logistic",    # binary classification 
              "eval_metric" = "error",    # evaluation metric 
              "nthread" = 8,   # number of threads to be used 
              "max_depth" = 16,    # maximum depth of tree 
              "eta" = 0.2,    # step size shrinkage 
              "gamma" = 0,    # minimum loss reduction 
              "subsample" = 1,    # part of data instances to grow tree 
              "colsample_bytree" = 1,  # subsample ratio of columns when constructing each tree 
              "min_child_weight" = 12)  # minimum sum of instance weight needed in a child 
 # Split back into test and train sets
train_xg <- combi_fg[1:891,]
test_xg <- combi_fg[892:1309,]

#Convert the data to matrix form:
#Convert the train_xg:
train.matrix = as.matrix(train_xg)
mode(train.matrix) = "numeric"
#Convert the test_xg:s
test.matrix = as.matrix(test_xg)
mode(test.matrix) = "numeric"

# k-fold cross validation, with timing
nround.cv = 200
xgboost.cv <- xgb.cv(param=param, data=train.matrix, label=train$Survived, 
                      nfold=10, nrounds=nround.cv, prediction=TRUE, verbose=T)

However when I am trying to predict on the test data using:

pred <- predict(xgboost.cv,test.matrix)

I am getting an error:

Why is this error coming and how to resolve it??

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