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+# 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:linear to do linear classification
+# when labels are in [0,1] we can also use reg:logistic
+objective = reg:linear
+
+# 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"
+