Gradient boosting decision tree (GBDT)
GBDT is an ensemble classifier based on bootstrap sampling, which aims to improve the generalization ability and robustness by combining the prediction results of multiple base learners (i.e. weak decision trees). The weight is adjusted with iteration, so that the higher weight will be assigned to the data poorly classified. Totally 15 weak decision trees were created in GBDT model in this study (e.g. a tree is showed in Fig The following parameter factors were used for GBDT: learning_rate = 0.1, max_depth = 8, random_state = 0, min_samples_leaf = 2. Other parameters were also set as default in the sklearn software.