37 lines
1.0 KiB
Plaintext
37 lines
1.0 KiB
Plaintext
# General Parameters, see comment for each definition
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# choose the tree booster, 0: tree, 1: linear
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booster_type = 0
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# this is the only difference with classification, use 0: linear regression
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# when labels are in [0,1] we can also use 1: logistic regression
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loss_type = 0
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objective="rank:pairwise"
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#objective="rank:softmax"
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#objective="lambdarank:map"
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#objective="lambdarank:ndcg"
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num_feature=50
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# Tree Booster Parameters
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# step size shrinkage
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bst:eta = 1.0
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# minimum loss reduction required to make a further partition
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bst:gamma = 1.0
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# minimum sum of instance weight(hessian) needed in a child
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bst:min_child_weight = 1
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# maximum depth of a tree
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bst:max_depth = 3
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# Task parameters
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# the number of round to do boosting
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num_round = 2
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# 0 means do not save any model except the final round model
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save_period = 0
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# The path of training data
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data = "mq2008.train"
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# The path of validation data, used to monitor training process, here [test] sets name of the validation set
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eval[test] = "mq2008.vali"
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# The path of test data
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test:data = "mq2008.test"
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