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研究生： 喬琳
Lynn Chiao
論文名稱： Predictive Models for Employee Voluntary Turnover: An Empirical Study of a Manufacturing Company in Taiwan
指導教授： 鄭晉昌
Jihn-Chang Jehng
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 人力資源管理研究所
Graduate Institute of Human Resource Management
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 52
中文關鍵詞： 機器學習 、人員自願性離職 、特徵選取 、預測模型 、監督式分類
外文關鍵詞： Machine learning, Employee voluntary turnover, Feature selection, Predictive model, Supervised classification
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    • 本研究以台灣某製造業公司的人員資料進行分析，使用羅吉斯迴歸、支持向量機、決策樹、隨機森林和極限梯度提升這五種監督式機器學習演算法，建立人員自願性離職的預測模型。除此之外，研究同時探討不平衡資料、特徵選取與K-摺疊交叉驗證的處理技術。結果顯示，隨機森林與極限梯度提升的預測表現最佳，兩個模型的F分數與AUC值均達0.85以上，代表模型有良好的鑑別度，能有效預測人員是否會選擇離職。透過分析變數重要性，研究發現人員的年齡、年資、初階管理訓練時數、專業訓練時數與平均晉升次數皆是用來判斷人員是否會選擇離職的主要依據。

    關鍵詞：機器學習、人員自願性離職、特徵選取、預測模型、監督式分類

    This study collects data from a manufacturing company in Taiwan. Logistic regression, support vector machine, decision tree, random forest, and eXtreme Gradient Boosting algorithms are adopted in order to build a reliable predictive model to predict employee voluntary turnover. Moreover, imbalanced classification problem, feature selection and K-fold cross validation are introduced and tested in this study. The results suggest random forest and eXtreme Gradient Boosting perform the best, both predictive models have the F-Score and AUC values above 0.85. Results of variable importance show elementary level of managerial training hours, professional training hours, average number of promotions, job tenure, and age contribute the most in predicting employee voluntary turnover outcome.

    Keywords: Machine learning, Employee voluntary turnover, Feature selection, Predictive model, Supervised classification

    CHAPTER 1: INTRODUCTION 1 1.1 Background of the Study 1 1.2 Statement of the Problem 2 1.3 Research Purpose and Objectives 3 CHAPTER 2: LITERATURE REVIEW 5 2.1 The Definition of Employee Turnover 5 2.2 Warning Signs and Causes of Employee Voluntary Turnover 5 2.3 Types of Machine Learning 6 2.3.1 Supervised Learning 7 2.3.2 Unsupervised Learning 7 2.4 Application of Machine Learning in the Field of Human Resource 8 CHAPTER 3: RESEARCH METHODS 9 3.1 Overview of Data 10 3.2 Training Set and Test Set Ratio 13 3.3 Machine Learning Algorithms 14 3.3.1 SVM 14 3.3.2 Logistic Regression 14 3.3.3 Decision Tree 14 3.3.4 Random Forest 15 3.3.5 XGBoost 15 3.4 Advanced Training Techniques 15 3.4.1 Imbalanced Class Classification 16 3.4.2 Hyper-parameter Tuning 17 3.4.3 Feature Selection 18 3.5 Evaluation Metrics for Classification 18 3.6 Receiver Operating Characteristic Curve (ROC) and Area under the curve (AUC) 20 CHAPTER 4: RESULTS 22 4.1 Descriptive Statistics 22 4.2 Classifier Performance 24 4.3 K-Fold Cross Validation with Grid Search 25 4.4 SMOTE 27 4.5 Boruta 28 4.6 SMOTE with Boruta 28 4.7 Variable Importance 29 CHAPTER 5: DISCUSSION & CONCLUSION 31 5.1 Discussion 31 5.1.1 Managerial Implications 32 5.1.2 Limitations of the Study 34 5.1.3 Suggestions for Future Research 34 5.2 Conclusion 35 REFERENCES 36

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