数据挖掘在“岗课赛证”融合课程中的应用研究

Research on the Application of Data Mining in the Integration Course of ''Post Course Competition Certificate''

数据挖掘方法在“岗课赛证”融合课程中的应用,存在挖掘结果交叉熵损失值过大、拟合程度过高等问题。为此,文章引入Python语言,开展对“岗课赛证”融合课程数据挖掘方法的设计研究。利用Python语言,挖掘“岗课赛证”融合课程数据;提取“岗课赛证”融合课程特征;结合Python中的sklearn库,实现对融合课程数据的挖掘与分类。实验结果表明,新挖掘方法得到的挖掘结果交叉熵损失值得到有效控制,拟合程度大大降低,使挖掘结果与实际相符。

In the application of data mining in the fusion course of''on-the-job course competition certificate'',there are problems such as excessive cross entropy loss and high fitting process in the mining results.This article introduces Python language to carry out research on the design of data mining methods for the fusion course of''on-the-job course competition certificate''.Using Python language to collect''on-the-job course competition certificate''fusion course data;Extract the characteristics of the integrated curriculum of''on-the-job course competition certificate'';Combining the sklearn library in Python to achieve mining and classification of fused course data.The experimental results show that the cross entropy loss of the mining results is effectively controlled,and the degree of fitting is greatly reduced,making the mining results consistent with the actual situation.