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Table 1 Main parameter settings of the models

From: Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Model type Main parameter Parameter value
XL booster gbtree
n_estimator 500
learning_rate 0.05
gamma 0
subsample 0.5
colsample_bytree 0.8
max_depth 10
eval_metric logloss
min_child_weight 6
LL boosting_type gdbt
learning_rate 0.1
num_leave 50
max_depth 6
num_leaves 64
min_child_samples 20
min_child_weight 0.002
reg_lambda 0.03
CDC type of forests Completely random tree forest, random forest
Multi-grained scanning Forests 2
Trees in each forest 1000
Sliding window size {151/16, 151/18, 151/4}
Cascade Forests 8
Trees in each forest 500