2026-10-13T22:38:21Z https://www.scidb.cn/oai

10.57760/sciencedb.06267 2022-11-09T16:34:29Z

2022-11-09 Code for Training and Optimizing Random Forest doi:10.57760/sciencedb.06267 en This code optimizes the performance of random forest by maximizing the generalization ability of the model.Outlines:Tune the hyperparameter ntree (the number of trees in the forest) and mtry (the number of variables allowed to be tried in each tree).Traverse the average error rate of the random forest on the out-of-bag data under every possible setting of these two hyperparameters through the nested loops. Determine the final setting of the two hyperparameters when the average error rate of the random forest on the out-of-bag data is minimum. Use the final setting of ntree and mtry to generate the random forest, which has the strongest generalization ability. Thus, optimizing the performance of the model. Random Forest; Optimization of Model; Hyperparameter Tuning Ying Yan Qi Huang PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank