28 lines
887 B
Plaintext
28 lines
887 B
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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# choose logistic regression loss function for binary classification
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loss_type = 2
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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 = "agaricus.txt.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] = "agaricus.txt.test"
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# The path of test data
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test:data = "agaricus.txt.test"
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