1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
29 lines
1005 B
Plaintext
29 lines
1005 B
Plaintext
# General Parameters, see comment for each definition
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# choose the tree booster, can also change to gblinear
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booster = gbtree
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# this is the only difference with classification, use reg:squarederror to do linear classification
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# when labels are in [0,1] we can also use reg:logistic
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objective = reg:squarederror
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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 = "machine.txt.train?format=libsvm"
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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] = "machine.txt.test?format=libsvm"
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# The path of test data
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test:data = "machine.txt.test?format=libsvm"
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