# General Parameters, see comment for each definition
# choose the tree booster, can also change to gblinear
booster = gbtree
# this is the only difference with classification, use reg:squarederror to do linear classification
# when labels are in [0,1] we can also use reg:logistic
objective = reg:squarederror

# 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 = 5

base_score = 2001
# Task parameters
# the number of round to do boosting
num_round = 100
# 0 means do not save any model except the final round model
save_period = 0
# The path of training data
data = "yearpredMSD.libsvm.train"
# The path of validation data, used to monitor training process, here [test] sets name of the validation set
eval[test] = "yearpredMSD.libsvm.test"
# The path of test data
#test:data = "yearpredMSD.libsvm.test"