# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 6 Discussion
### Model Scope and Specificity  
A critical decision in predictive modeling for employee absenteeism is choosing between a global model (such as the models described in this paper), which integrates data across various companies, regions, and industries, opposite to more localized models tailored to specific domains or regions. Global models benefit from larger datasets, potentially improving the model's generalizability and robustness. However, they may overlook nuanced patterns specific to certain industries or regions, such as cultural differences in work habits or regional legal differences that affect absenteeism.  
Conversely, localized models can capture these nuances by focusing on data from specific sectors or regions. They might reveal unique predictors of absence that are not evident in a broader dataset. For example, factors that influence absenteeism in a tech company can differ significantly from those of a manufacturing plant due to varying work environments and employee demographics. Similarly, regional models might adapt better to local economic conditions, public holidays, or weather patterns that influence absence rates.  
Exploring the transferability of models between sectors or regions is another potential area of research. By testing how models trained in one sector perform when applied to another, or how models developed using data from one region predict absences in a different region, researchers can assess the adaptability and flexibility of their predictive approaches. Such experiments can help identify core, universal predictors of absenteeism across domains and highlight specific factors that are regionally or sector-specific.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Question** & **Findings** \\ \hline Q1: How well can we predict one week ahead of an employee absence using historical data? & Bagging methods performed best for vacation leave, while Random Forest was most effective for sick leave, demonstrating strong predictive accuracy in both cases. \\ \hline Q2: How effective are the predictive models at forecasting two weeks ahead of employee absence? & Optimal predictions were achieved one week in advance using the best methods identified for each type of leave, with Bagging and Random Forest outperforming other models. \\ \hline Q3: What is the predictive accuracy for one-month-ahead employee absence predictions? & Bagging continued to show superior results for vacation leave predictions, and Random Forest excelled in sick leave predictions even at this extended forecast interval. \\ \hline Q4: How do single-target vs. multi-target prediction models perform in predicting employee absence? & Bagging consistently demonstrated high performance across both single-target and multi-target prediction scenarios, affirming its robustness in handling various prediction tasks. \\ \hline Q5: How effective are the ensemble methods compared to the single models in predicting employee absence across different time frames? & Ensemble methods, particularly Bagging and Random Forest, consistently outperformed single predictive models, highlighting their greater robustness against overfitting and enhanced predictive accuracy. \\ \hline Q6: How do attributes impact predictive models and which are the most influential in predicting one week, two weeks, and one month ahead? & Feature importance analysis revealed that time-related attributes and specific absence history metrics (such as past sick leave and vacation patterns) were critical to enhance the predictive performance of the models. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of research question findings.  
Finally, building models on data from a single company brings some challenges. There are risks of overfitting the particularities of the company's data and the possibility to generalize to new employees or underrepresented groups within the company.