wehub-resource-sync
69 lines
1.7 KiB
Plaintext
69 lines
1.7 KiB
Plaintext
# task type, support train and predict
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task = train
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# boosting type, support gbdt for now, alias: boosting, boost
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boosting_type = gbdt
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# application type, support following application
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# regression , regression task
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# binary , binary classification task
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# lambdarank , LambdaRank task
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# multiclass
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# alias: application, app
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objective = multiclass
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# eval metrics, support multi metric, delimited by ',' , support following metrics
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# l1
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# l2 , default metric for regression
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# ndcg , default metric for lambdarank
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# auc
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# binary_logloss , default metric for binary
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# binary_error
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# multi_logloss
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# multi_error
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# auc_mu
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metric = multi_logloss,auc_mu
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# AUC-mu weights; the matrix of loss weights below is passed in parameter auc_mu_weights as a list
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# 0 1 2 3 4
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# 5 0 6 7 8
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# 9 10 0 11 12
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# 13 14 15 0 16
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# 17 18 19 20 0
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auc_mu_weights = 0,1,2,3,4,5,0,6,7,8,9,10,0,11,12,13,14,15,0,16,17,18,19,20,0
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# number of class, for multiclass classification
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num_class = 5
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# frequency for metric output
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metric_freq = 1
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# true if need output metric for training data, alias: tranining_metric, train_metric
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is_training_metric = true
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# column in data to use as label
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label_column = 0
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# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
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max_bin = 255
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# training data
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# if existing weight file, should name to "regression.train.weight"
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# alias: train_data, train
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data = multiclass.train
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# valid data
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valid_data = multiclass.test
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# round for early stopping
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early_stopping = 10
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# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
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num_trees = 100
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# shrinkage rate , alias: shrinkage_rate
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learning_rate = 0.05
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# number of leaves for one tree, alias: num_leaf
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num_leaves = 31
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