Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary Algorithm

Table 3

Some of important parameters considered for different methods.

Method	Parameters
XGBoost	Number of boosting stages = 100; loss function = “log_loss”; Learning rate = 0.1; Maximum depth of the individual regression estimators = 3
RandomForest_1	The number of trees in the forest = 20 measures the quality of a split = “gini”; The number of features to consider when looking for the best split = “sqrt (n_features)”
RandomForest_2	The number of trees in the forest = 30 measures the quality of a split = “gini”; The number of features to consider when looking for the best split = “sqrt (num of features)”
Logistic regression	Solver = “lbfgs” penalty term = “L2”; Tolerance for stopping criteria = 1e-4