# General Parameters, see comment for each definition
# choose the booster, can be gbtree or gblinear
booster = gbtree
# choose logistic regression loss function for binary classification
objective = binary:logistic

# Tree Booster Parameters
# step size shrinkage
eta = 1.0 
# minimum loss reduction required to make a further partition
gamma = 1.0 
# minimum sum of instance weight(hessian) needed in a child
min_child_weight = 1 
# maximum depth of a tree
max_depth = 3 

# Task Parameters
# the number of round to do boosting
num_round = 2
# 0 means do not save any model except the final round model
save_period = 0 
use_buffer = 0

# The path of training data %d is the wildcard for the rank of the data
# The idea is each process take a feature matrix with subset of columns
#
data = "train.row%d" 

# The path of validation data, used to monitor training process, here [test] sets name of the validation set
eval[test] = "../../demo/data/agaricus.txt.test" 
# evaluate on training data as well each round
eval_train = 1

# The path of test data, need to use full data of test, try not use it, or keep an subsampled version
test:data = "../../demo/data/agaricus.txt.test"