[Retracted] PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost

Table 3

Parameter setting by grid search for XGBoost.

Parameter	Range	Used	Description
max_depth	[3, 4, 5, 6, 7, 8, 9, 10]	3	Maximum tree depth for base learners
min_child_weight	[1, 2, 3, 4, 5]	4	Minimum sum of instance weight (hessian) needed in a child
gamma	[0, 0.1, 0.2, 0.3, 0.4, 0.5]	0.2	Minimum loss reduction required to make a further partition on a leaf
subsample	[0.6, 0.7, 0.8, 0.9, 1]	0.8	Subsample ratio of the training instance
colsample_bytree	[0.6, 0.7, 0.8, 0.9, 1]	0.8	Subsample ratio of columns when constructing each tree
reg_alpha	[1e − 5, 1e − 4, 1e − 3, 1e − 2, 0.1, 1, 100]	1e − 05	L1 regularization term on weights
reg_lambda	[0.05, 0.1, 1, 2, 3]	1	L2 regularization term on weights
learning_rate	[0.01, 0.05, 0.07, 0.1, 0.2]	0.07	Boosting learning rate