29 lines
771 B
Plaintext
29 lines
771 B
Plaintext
# General Parameters, see comment for each definition
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# choose the booster, can be gbtree or gblinear
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booster = gbtree
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# choose logistic regression loss function for binary classification
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objective = binary:logistic
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# Tree Booster Parameters
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# step size shrinkage
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eta = 1.0
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# minimum loss reduction required to make a further partition
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gamma = 1.0
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# minimum sum of instance weight(hessian) needed in a child
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min_child_weight = 1
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# maximum depth of a tree
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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 = stdin
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# The path of model file
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model_out = stdout
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# evaluate on training data as well each round
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eval_train = 1
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