Abstract: When the Xgboost framework is in use, it is often involved in the adjustment of various parameters, and the selection of parameters has a great influence on the classification performance of the model. The traditional parameter optimization method usually first derives a penalty function, and then the empirical or exhaustive method is used to adjust the parameter value to maximize or minimize the penalty function, but often encounters a model without an explicit expression. The optimization of the parameters of this model is very troublesome, also bringing some uncertainty and randomness to the algorithm. The Bayesian optimization algorithm based on Gaussian method (GP) is used to optimize the parameters of the Xgboost framework. A new algorithm, GP_Xgboost, is proposed and experimented by multiple sets of numerical values. The results show that the proposed algorithm is superior to the manual tuning and exhaustive method, which proves the feasibility and effectiveness of the proposed algorithm.

Key words: Xgboost algorithm, model parameters, Bayesian optimization, parameter optimization