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We set parameters including n_estimators of 500 (i.e., boosting stages were 500.), max_depth of 4 (i.e., the number of nodes in the tree is limited to 4.), min_samples_split of 5 (i.e., the minimum number of samples required to split an internal node was 5.), learning_rate of 0.01 (i.e., the contribution of each tree would shrink by 0.01.), loss of squared_error (i.e., the least squares function was used as the loss function to optimize.)

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