# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 6 Discussion  
### Summary of Research Question Findings  
In Table 10, we present a summary of the findings for each research question. Our analysis revealed key insights into the performance of various predictive models across different time horizons for employee absenteeism (Q1-Q3). The ensemble methods, particularly Bagging and Random Forests, consistently outperformed single models like regression trees. This superiority was evident across one-week, two-week, and one-month prediction intervals, underscoring the robustness of ensemble approaches to capture complex patterns and variances in the data. Bagging, in particular, showed the most consistent performance, offering the lowest error rates and the highest correlation coefficients, thus providing a reliable model for forecasting employee absences.  
The comparison between single-target and multi-target approaches (Q4-Q5) further highlighted that while single-target models are straightforward and easier to implement, multi-target models often yield slightly better performance, particularly in longer prediction windows. This suggests that capturing interdependencies between consecutive weeks can enhance predictive accuracy, a crucial insight for HR departments planning over longer periods.  
Finally, the feature importance analysis (Q6) highlighted that time-related attributes and specific absence history metrics, such as past sick leave and vacation patterns, were crucial in improving the predictive performance of the models.  
The practical implications of these findings are significant. The demonstrated efficacy of ensemble methods and multi-target models can guide HR analytics strategies, encouraging the adoption of more complex, but more accurate, predictive tools. These tools can be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**One-Week Ahead Prediction** & **Two-Weeks Ahead Prediction** & **One-Month Ahead Prediction** \\ \hline Previous12 MonthSickLeaveDay5 & Previous6 MonthSickLeaveDay5 & Previous12 MonthSickLeaveDay5 \\ \hline Previous12 MonthVacationLeaveDay4 & Previous12 MonthVacationLeaveDay5 & Previous12 MonthVacationLeaveDay5 \\ \hline Previous3 MonthVacationLeaveDay5 & Previous6 MonthSickLeaveDay4 & Previous12 MonthSickLeaveDay4 \\ \hline Previous6 MonthVacationLeaveDay5 & Previous12 MonthSickLeaveDay4 & Previous12 MonthVacationLeaveDay4 \\ \hline Previous1 MonthSickLeaveDay6 & Previous2 WeekSickLeaveDay6 & Previous6 MonthVacationLeaveDay4 \\ \hline Previous3 MonthSickLeaveDay5 & Previous3 MonthVacationLeaveDay5 & Previous12 MonthVacationLeaveDay2 \\ \hline Previous12 MonthVacationLeaveDay1 & Previous12 MonthVacationLeaveDay2 & Week\_SummerSeason\_K+1 \\ \hline Previous12 MonthSickLeaveDay3 & Previous12 MonthVacationLeaveDay3 & Week\_SummerHoliday\_K+4 \\ \hline Previous6 MonthSickLeaveDay4 & Previous6 MonthVacationLeaveDay4 & Week\_SpringSeason\_K+1 \\ \hline Previous6 MonthVacationLeaveDay4 & Previous3 MonthSickLeaveDay3 & Week\_SummerHoliday\_K+3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Attribute importance using Random Forests for sick leave prediction with forecasting horizons of one week, two weeks, and one month. Attributes are presented in descending order of importance, as determined by the impurity function. Only the top ten attributes are shown.  
instrumental in helping HR professionals anticipate and plan for potential staffing gaps, thereby ensuring that operational efficiency is maintained.  
Furthermore, the varying performance across different time horizons raises important questions about the optimal frequency of model updates and the potential need for different modelling approaches depending on the specific forecasting needs of the organization. As we move towards integrating these models into HR decision-making processes, understanding their performance nuances becomes critical in tailoring interventions that are both proactive and contextually appropriate.