This paper proposes an improved Stacking-based teacher score prediction model for the problem of weak generalisation ability of a single model,firstly using LightGBM for feature selection,then Box-Cox transformation for target values,and finally using Stacking-based regression model that integrates AdaBoost,RF,XGBoost,KRR algorithms,and then the Stacking model is weighted with a single learner Ridge combination to predict teacher scores.Aiming at the problem of weak generalization ability of a single model,a teacher score prediction model based on improved Stacking model is proposed.The experimental results show that the root mean square error of the improved algorithm on the teacher scores dataset is 9.715,which is 0.227 lower than the regression algorithm AdaBoost and 0.161 lower than the traditional Stacking fusion model.
Educational data miningStudent assessment of teachingStacking integrated learningFeature selection
NETL
NSTL
万方数据
