chore: import upstream snapshot with attribution

examples/binary_classification/train_linear.conf

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+# task type, support train and predict
+task = train
+
+# boosting type, support gbdt for now, alias: boosting, boost
+boosting_type = gbdt
+
+# application type, support following application
+# regression , regression task
+# binary , binary classification task
+# lambdarank , LambdaRank task
+# alias: application, app
+objective = binary
+
+linear_tree = true
+
+# eval metrics, support multi metric, delimited by ',' , support following metrics
+# l1
+# l2 , default metric for regression
+# ndcg , default metric for lambdarank
+# auc
+# binary_logloss , default metric for binary
+# binary_error
+metric = binary_logloss,auc
+
+# frequency for metric output
+metric_freq = 1
+
+# true if need output metric for training data, alias: tranining_metric, train_metric
+is_training_metric = true
+
+# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
+max_bin = 255
+
+# training data
+# if existing weight file, should name to "binary.train.weight"
+# alias: train_data, train
+data = binary.train
+
+# validation data, support multi validation data, separated by ','
+# if existing weight file, should name to "binary.test.weight"
+# alias: valid, test, test_data,
+valid_data = binary.test
+
+# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
+num_trees = 100
+
+# shrinkage rate , alias: shrinkage_rate
+learning_rate = 0.1
+
+# number of leaves for one tree, alias: num_leaf
+num_leaves = 63
+
+# type of tree learner, support following types:
+# serial , single machine version
+# feature , use feature parallel to train
+# data , use data parallel to train
+# voting , use voting based parallel to train
+# alias: tree
+tree_learner = serial
+
+# number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count.
+# num_threads = 8
+
+# feature sub-sample, will random select 80% feature to train on each iteration
+# alias: sub_feature
+feature_fraction = 0.8
+
+# Support bagging (data sub-sample), will perform bagging every 5 iterations
+bagging_freq = 5
+
+# Bagging fraction, will random select 80% data on bagging
+# alias: sub_row
+bagging_fraction = 0.8
+
+# minimal number data for one leaf, use this to deal with over-fit
+# alias : min_data_per_leaf, min_data
+min_data_in_leaf = 50
+
+# minimal sum Hessians for one leaf, use this to deal with over-fit
+min_sum_hessian_in_leaf = 5.0
+
+# save memory and faster speed for sparse feature, alias: is_sparse
+is_enable_sparse = true
+
+# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
+# alias: two_round_loading, two_round
+use_two_round_loading = false
+
+# true if need to save data to binary file and application will auto load data from binary file next time
+# alias: is_save_binary, save_binary
+is_save_binary_file = false
+
+# output model file
+output_model = LightGBM_model.txt
+
+# support continuous train from trained gbdt model
+# input_model= trained_model.txt
+
+# output prediction file for predict task
+# output_result= prediction.txt
+
+
+# number of machines in distributed training, alias: num_machine
+num_machines = 1
+
+# local listening port in distributed training, alias: local_port
+local_listen_port = 12400
+
+# machines list file for distributed training, alias: mlist
+machine_list_file = mlist.txt
+
+# force splits
+# forced_splits = forced_splits.json