Transformative Methods to Fire Credit Rating: Integrating Data Mining Methods and XGBoost

Under the background of fire credit rating XGBoost, to improve the success rate of integrated data mining method optimization, the optimization of integrated data mining method of fire credit rating is studied, but when the traditional method is used for the optimization evaluation of integrated data mining method, there is the problem of poor evaluation accuracy. To this end, an integrated data mining method based on fire credit rating is proposed. Firstly, the fuzzy evaluation theory is adopted to integrate the set of evaluation factors optimized by the data mining method to calculate the weight of each factor and obtain the ratio of the weight of each factor. Then, the integrated data mining method is used to optimize the integrated data mining method, combined with the integrated data mining method, and the evaluation value of the success rate, and complete the optimization of the integrated data mining method under the background of fire credit rating XGBoost. According to the simulation experiment results, the proposed method can effectively improve the effectiveness and reliability of the integrated data mining method.