Extreme gradient boosting algorithm in employment prediction of college graduates

With the rapid development of science and technology, machine learning algorithms are applied to classification prediction and other fields by piping rice because of their automatic learning characteristics and powerful generalization ability. Aiming at the current problem of low efficiency of employment guidance for college graduates, the study proposes an extreme gradient boosting algorithm. And for the problem of feature selection of college graduates, filtered and wrapped feature selection algorithms are introduced, and a hybrid feature selection algorithm is constructed by combining the two. Finally, the extreme gradient boosting algorithm and the hybrid algorithm are combined to construct a college graduate employment prediction model based on them. The college graduate employment prediction model is analyzed for the comparison of prediction accuracy and recovery rate, and the results show that the average prediction accuracy and average recovery rate of the model are 97.26% and 96.7%, respectively, which are better than the comparison model. The above results show that the model proposed in the study has good performance and effectiveness, which helps to improve the efficiency of college graduate employment guidance.