有序Lasso-Logistic模型的电竞角色选择应用分析

Analysis of Role Selection in E-sports Based on Ordered Lasso-Logistic Model

电竞行业近几年来迅速发展,这其中离不开机器学习在电竞中的分析和应用。职业选手能够熟悉电竞角色往往需要一定的时间和经验,采用机器学习对电竞角色进行分析,有利于选手对角色选择的考虑,为职业选手的训练和比赛提供数据支持,体现预测模型的分析效果。基于电竞结果通常为二分类数据,为了更好地利用先验信息,将自变量对因变量发生的不同重要性归结到Lasso-Logistic模型中形成有序Lasso-Logistic模型,通过有序Lasso-Logistic模型分析电竞角色数据,对电竞结果进行预测。根据结果显示,有序Lasso-Logistic模型预测效果显著,将有序Lasso-Logistic模型预测结果与逻辑回归模型,Lasso-Logistic模型,梯度增强决策树模型以及SVM模型的预测结果相比较,增加了先验信息的有序Lasso-Logistic模型的预测效果明显比其他四个机器学习模型更佳,表明了有序Lasso-Logistic模型在机器学习中对分类数据相比经典分类模型具有更好的分析能力,突出自变量先验信息重要的作用,推动了机器学习在电子竞技行业的应用与发展。

The e-sports industry has developed rapidly in recent years,which cannot be separated from the analysis and application of machine learning.Professional players can be familiar with the e-sports roles usually need a certain amount of time and experience.Analyzing the e-sports roles by machine learning is conducive to the player’s consideration of roles selection and provides data support for the training and competition of professional players,showing performance of predicted model.The e-sports results are usually binary data.In order to make better use of prior information,the different importance of independent variables to the occurrence dependent variables is combined with Lasso-Logistic model to form an ordered Lasso-Logistic model.We analyze the e-sports roles data by ordered Lasso-Logistic model and predict the competition results.According to the results,the ordered Lasso-Logistic model has a significant prediction effect.Taking the performance of the ordered Lasso-Logistic model compared with the logistic regression model,the Lasso-Logistic model,the Gradient Boosting Decision Tree model and the SVM model,we found that the prediction of ordered Lasso-Logistic definitely better than the others,which suggests that the ordered model is the better analytical skills for classified data in machine learning and importance of prior information of independent variables and promoted the application and development of machine learning in e-sports industry.