decision tree, random forest uses large number of trees to overcome the overfitting problem and when the tree number became larger, the performance became much more stable. Boosting also make the performance of algorithm stable and better by fix the initial model with the shrunken version of the following trees. These two methods also showed strong ability on selecting the best models directly. Among these five methods, linear model and decision got the smallest system cost, and it is very fast. Neural Network is more complex but the procedure is not time consuming. Comparing with them, boosting and random forest will generate a very large model which can arrived a few gigabytes, and consume much more time for generating large number of decision trees. In this study, even though different machine learning methods showed really good performance, more feature work is still needed. The first problem is some of the applications used to generate features are not the newest version, by update these applications, the quality of features may be improved. The second issue is the tree number of random forest and boosting just reached 3,000 and 1,000 in this work, but in some other researches [36], this parameter is tested up to 10000. Especially when testing boosting, it still showed a tendency of decreasing on quality lost when the tree number grew. Another important problem is that though random forest and boosting result in a lower correlation than consensus-based method, they still got a better performance on quality lost, this may hints machine learning methods is more suitable on some kinds of special targets or models. And on some targets that consensus-based
decision tree, random forest uses large number of trees to overcome the overfitting problem and when the tree number became larger, the performance became much more stable. Boosting also make the performance of algorithm stable and better by fix the initial model with the shrunken version of the following trees. These two methods also showed strong ability on selecting the best models directly. Among these five methods, linear model and decision got the smallest system cost, and it is very fast. Neural Network is more complex but the procedure is not time consuming. Comparing with them, boosting and random forest will generate a very large model which can arrived a few gigabytes, and consume much more time for generating large number of decision trees. In this study, even though different machine learning methods showed really good performance, more feature work is still needed. The first problem is some of the applications used to generate features are not the newest version, by update these applications, the quality of features may be improved. The second issue is the tree number of random forest and boosting just reached 3,000 and 1,000 in this work, but in some other researches [36], this parameter is tested up to 10000. Especially when testing boosting, it still showed a tendency of decreasing on quality lost when the tree number grew. Another important problem is that though random forest and boosting result in a lower correlation than consensus-based method, they still got a better performance on quality lost, this may hints machine learning methods is more suitable on some kinds of special targets or models. And on some targets that consensus-based