Gradient boosting machine (GBM) is also an ensemble machine learning technique to construct the multiple decision trees in a step-wise manner. Each decision tree is not randomly generated as in the random forest, but is consecutively built to give a better estimate of the response variable. More specifically, GBM is to stepwisely construct a new decision tree as a weak learner with the maximum correlation to the negative gradient of the loss function (Friedman, 2002). The number of decision trees (10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1,000), and the learning rate (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) will be tried during the cross-validation.