# xgboost logistic regression predictions are returning values >1 and < 0

I'm currently using xgboost to try and fit a logistic model with a binary outcome on a set of training data, but when I use the model that I get from this training data on a new set of classified test data, the predictions I'm getting back give me probabilities that are greater than 1 and less than 0. I've noticed that predictions on the training data are also prone to this problem, but on the test data I'm getting probabilities > 1.2 and < -0.1, whereas the training data's predicted values that exceed 1 and 0 are something like 1.0001 and -0.001.

What would cause predicting with the xgboost model to do this? Here's the parameters I'm passing, the line for building the model, and the line for prediction that I'm using:

# Select xgboost parameters
parameters = list(eta = 0.005,
max_depth = 15,
subsample = 0.5,
colsample_bytree = 0.5,
seed = 1,
eval_metric = "auc",
objective = "binary:logistic")

# Fit a model
xgb_reduced = xgboost(data = xgbtrain_reduced, parameters,
nround = which.max(cvreduced$evaluation_log$test_auc_mean))

# Load a file without also loading the name of the R object that the file is storing
load.to.prompt = function(file_str){
tmp = load(file_str)
return(get(tmp))
}

# Do prediction
classifier = load.to.prompt(model.file)