XGBoost Improved Method Based on Bayesian Optimization and Combination Model
In the field of regression prediction,models and hyperparameters have a great influence on the prediction results.The appropriate model can determine the prediction effect,while the hyperparameters of the model directly control the way of train-ing,and the use of appropriate hyperparameters can further improve the accuracy of the algorithm.In order to further improve the prediction accuracy and robustness of the model,a regression optimization method based on Bayesian optimization and combination model is proposed.Firstly,the fast global search ability of Bayesian optimization is used to optimize XGBoost algorithm and LightG-BM algorithm with the average score of cross validation as the objective function value,a better super parameter value is chosen to establish BO_XGBoost and BO_LightGBM model,and then the sequential minimum programming algorithm is used to determine the BO_XGBoost and Bo_LightGBM model weight for model combination.On UCI data sets and the results show that the method can improve the prediction accuracy and robustness effectively.
万方数据
维普
