Winning Rate Prediction for Moba Games Based on Ensemble Learning Algorithms
With the popularization of technology and the Internet,e-sports has become more and more popular.It has become an official event of the Hangzhou Asian Games and a sports competition.In traditional sports industries such as football and basketball,the application of big data technology is quite mature.However,for emerging esports media,the use of data mining,machine learning,and neural networks to predict game results is particularly crucial.Take the moba game dota2 as an example to analyze the winning rate.Dota 2,as a globally renowned esports event,has a large audience and open data.The outcome of a competition is influenced by various factors.Extract important features from player match data and train word vectors using neural networks.Compare multiple traditional and en-semble learning algorithms to select models with excellent performance.Finally,the SHAP model is used to visualize and analyze the machine learning process,summarizing important features.Assist team decision-making and enhance audience experience.
万方数据
维普
