зеркало из https://github.com/microsoft/LightGBM.git
115 строки
3.3 KiB
Plaintext
115 строки
3.3 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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# alias: application, app
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objective = binary
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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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metric = binary_logloss,auc
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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 "binary.train.weight"
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# alias: train_data, train
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data = binary.train
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# validation data, support multi validation data, separated by ','
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# if existing weight file, should name to "binary.test.weight"
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# alias: valid, test, test_data,
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valid_data = binary.test
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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 = 0.1
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# number of leaves for one tree, alias: num_leaf
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num_leaves = 63
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# type of tree learner, support following types:
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# serial , single machine version
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# feature , use feature parallel to train
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# data , use data parallel to train
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# voting , use voting based parallel to train
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# alias: tree
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tree_learner = serial
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# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count.
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# num_threads = 8
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# feature sub-sample, will random select 80% feature to train on each iteration
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# alias: sub_feature
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feature_fraction = 0.8
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# Support bagging (data sub-sample), will perform bagging every 5 iterations
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bagging_freq = 5
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# Bagging fraction, will random select 80% data on bagging
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# alias: sub_row
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bagging_fraction = 0.8
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# minimal number data for one leaf, use this to deal with over-fit
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# alias : min_data_per_leaf, min_data
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min_data_in_leaf = 50
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# minimal sum Hessians for one leaf, use this to deal with over-fit
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min_sum_hessian_in_leaf = 5.0
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# save memory and faster speed for sparse feature, alias: is_sparse
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is_enable_sparse = true
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# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
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# alias: two_round_loading, two_round
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use_two_round_loading = false
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# true if need to save data to binary file and application will auto load data from binary file next time
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# alias: is_save_binary, save_binary
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is_save_binary_file = false
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# output model file
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output_model = LightGBM_model.txt
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# support continuous train from trained gbdt model
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# input_model= trained_model.txt
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# output prediction file for predict task
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# output_result= prediction.txt
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# number of machines in distributed training, alias: num_machine
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num_machines = 1
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# local listening port in distributed training, alias: local_port
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local_listen_port = 12400
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# machines list file for distributed training, alias: mlist
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machine_list_file = mlist.txt
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# force splits
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# forced_splits = forced_splits.json
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