基于决策树分类算法的计算机大数据分析研究  

Research on Computer Big Data Analysis Based on Decision Tree Classification Algorithm

作　　者：赵静 孔陶茹[1] ZHAO Jing;KONG Taoru(Xi’an Siyuan University,Xi’an 710038,China)

机构地区：[1]西安思源学院,西安710038

出　　处：《自动化与仪器仪表》2024年第10期154-158,共5页Automation & Instrumentation

基　　金：陕西省教育科学“十四五”规划《数字化转型背景下教师数字胜任力影响机制及发展路径研究》(SGH23Y2869)。

摘　　要：随着大数据技术的快速发展,计算机大数据分析成为处理庞大数据集的重要工具。决策树分类算法作为常用的机器学习方法之一,被广泛应用于大数据分析领域。此次以基于决策树分类算法的计算机大数据分析研究为题,探讨了决策树分类算法在大数据分析中的应用与优化方法。通过实验得出,在性能验证实验中,提出模型在Test dataset1数据集上平均召回率达到了90.22%;在Test dataset2数据集上平均召回率达到了87.01%。实验结果显示,基于决策树分类算法的计算机大数据分析方法具有较高的准确性和可解释性,该模型的分析结果可以为决策制定提供重要参考。此外在应用分析中,提出模型面对干扰数据也有良好的表现,因此验证了此次研究对模型的改进具有正向作用。With the rapid development of big data technology,computer big data analysis has become an important tool for processing large datasets.The decision tree classification algorithm,as one of the commonly used machine learning methods,is widely used in the field of big data analysis.This study focuses on the research of computer big data analysis based on decision tree classification algorithm,exploring the application and optimization methods of decision tree classification algorithm in big data analysis.Through experiments,it was found that in the performance validation experiment,the proposed model achieved an average recall rate of 90.22%on the Test dataset1 dataset;On the Test dataset 2,the average recall rate reached 87.01%.The experimental results show that the computer big data analysis method based on decision tree classification algorithm has high accuracy and interpretability,and the analysis results of this model can provide important reference for decision-making.In addition,in the application analysis,it was proposed that the model also performed well in the face of interference data,thus verifying that this study has a positive effect on the improvement of the model.

关 键 词：大数据分析 决策树分类算法 特征选择 注意力机制 

分 类 号：TK018[动力工程及工程热物理] TP29[自动化与计算机技术—检测技术与自动化装置]