# General Parameters, see comment for each definition

# specify objective
objective="rank:pairwise"

# Tree Booster Parameters
# step size shrinkage
eta = 0.1
# 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 = 0.1
# maximum depth of a tree
max_depth = 6

# Task parameters
# the number of round to do boosting
num_round = 4
# 0 means do not save any model except the final round model
save_period = 0
# The path of training data
data = "mq2008.train"
# The path of validation data, used to monitor training process, here [test] sets name of the validation set
eval[test] = "mq2008.vali"
# The path of test data
test:data = "mq2008.test"