Any company's human resources department faces the challenge of predicting
whether an applicant will search for a new job or stay with the company. In
this paper, we discuss how machine learning (ML) is used to predict who will
move to a new job. First, the data is pre-processed into a suitable format for
ML models. To deal with categorical features, data encoding is applied and
several MLA (ML Algorithms) are performed including Random Forest (RF),
Logistic Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting
(XGBoost). To improve the performance of ML models, the synthetic minority
oversampling technique (SMOTE) is used to retain them. Models are assessed
using decision support metrics such as precision, recall, F1-Score, and
accuracy.

人力资源部门面临预测申请人是否寻找新工作或留在公司的挑战。本论文讨论了机器学习如何用于预测谁将转到新工作，采用数据预处理以适合机器学习模型，处理分类特征，应用数据编码和多个机器学习算法，包括随机森林、逻辑回归、决策树和极端梯度提升。为提高机器学习模型的性能，使用合成少数类过采样技术 (SMOTE) 进行改进，采用决策支持指标，如精确度、召回率、F1-Score 和准确度进行评估。