基于Logistic算法的考研成绩变量预测方法

针对传统考研成绩变量预测方法的变量关联性低,导致预测结果存在较大误差的问题,提出基于Logistic算法的考研成绩变量预测方法.收集并处理历年考研成绩数据和学生成绩数据,作为成绩变量预测的初始数据.设置考研成绩的预测变量,建立Logistic回归分类算法模型,通过该模型的运算提高考研成绩变量之间的关联性.综合历年考研成绩数据的发展规律以及变量的影响因素分析结果,得出考研成绩变量的预测结果.通过对比实验分析得出结论:基于Logistic算法的考研成绩变量预测方法的预测误差率较低,预测准确性较高.

Method of Predicting Performance Variables of Postgraduate Entrance Examination Based on Logistic Algorithm

In view of the low correlation of the variables in the traditional method, which leads to the big error in the prediction results, a method based on logistic algorithm is proposed to predict the performance variables of the postgraduate entrance examination. The data of postgraduate entrance examination and student achievement over the years is collected and processed as the initial data of achievement variable prediction. The predictive variables of the performance of the postgraduate entrance examination is set up, the logistic regression classification algorithm model is established, and the correlation between the performance variables through the operation of the model is improved. Based on the analysis of the development law of the data and the influencing factors of the variables, the prediction results of the variables are obtained. Through the comparative experimental analysis, it is concluded that the prediction error rate of the method based on logistic algorithm is low and the prediction accuracy is high.