Construction of a CatBoost Classification Prediction Model for Municipal-Level Teacher Identity Based on Professional Experience

To explore the relationship between teacher identity characteristics and professional experience, this study takes municipal-level outstanding teachers in the Beijing-Tianjin-Hebei region as the research sample and constructs a teacher identity classification prediction model based on the CatBoost machine learning algorithm. Guided by the principle of "promoting the great spirit of educators," this paper extracts 14 professional experience characteristic indicators from four dimensions: educational and teaching ability, collaboration and innovation ability, research and practice ability, among others. Leveraging the advantages of the CatBoost algorithm in handling categorical features, the study calculates the importance of various identity characteristics of outstanding teachers and applies them in the training and testing of variables. In terms of innovation, the CatBoost algorithm improves upon the traditional Gradient Boosting Decision Tree (GBDT) by refining the classification effectiveness through test data evaluation, thereby ensuring precise model assessment. The research findings indicate that machine learning has broad applicability in evaluating teacher professional development and can accurately reveal the composition of teacher professional identity. This provides strong data support and scientific evidence for advancing teacher professional development, formulating targeted training strategies, and promoting the construction of a strong education system.