Research on Prediction of Online Training Effect of Newly Recruited PE Teachers in Junior and Senior Middle Schools Based on Machine Learning

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With the continuous development of online education, how to improve the teaching ability of new PE teachers through adaptive and effective online training has become an important research issue. Based on machine learning algorithm, this paper discusses the influence of different characteristics on the adaptability of online training for new physical education teachers, and evaluates the application of various models in predicting the training effect of teachers. The results show that factors such as teachers' professional experience, past training experience, school type and whether to participate in training with colleagues have a significant impact on the adaptability of online training. By comparing Logistic regression model, KNN model, random forest model, XGBoost model and support vector machine model, this paper finds that random forest model is the best in prediction accuracy and generalization ability. This study provides data support and theoretical basis for optimizing the online training of physical education teachers, and can provide reference for educational managers to formulate personalized training programs.