# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 2 Background and Related Work
### Predicting Employee Absence with Predictive Clustering Trees  
In the methodological framework of our study, which aims to predict employee absenteeism, we used Predictive Clustering Trees (PCTs) [7], which are available in the CLUS Toolbox [8] ([[https://github.com/ElsevierSoftwareX/SOFTX-D-23-00369](https://github.com/ElsevierSoftwareX/SOFTX-D-23-00369)]([https://github.com/ElsevierSoftwareX/SOFTX-D-23-00369](https://github.com/ElsevierSoftwareX/SOFTX-D-23-00369)), accessed on 10 May 2024). PCTs offer a versatile approach to machine learning that goes beyond traditional decision trees by combining both clustering and predictive capabilities. These models are ideal for scenarios where both clustering of similar instances and prediction of outcomes are required. Essentially, PCTs build a tree where each node serves not only as a decision point for splitting data but also as a cluster that groups similar data points. Using this method, PCTs can process different data sets by recognizing and exploiting the inherent groupings within the data.  
In our study, PCTs were used in two different constellations: Single models (denoted as ST) in a regression setting to predict the number of days of absence and in a multi-target regression setting (denoted as MT), where the aim was to predict absences for several future periods simultaneously.  
As a baseline for comparison in our study, we have used a PCT of minimal complexity consisting of only one node. This simple model serves as a basic reference point that allows us to evaluate the effectiveness and added value of more complex models in predicting employee absenteeism.  
To improve the robustness and accuracy of our predictions, we have extended our use of PCTs to various ensemble methods [17]. Specifically, we used PCTs for Bagging, Random Subspaces (denoted as RSubspaces), Random Forest (denoted as RForest), and a combined method that integrates both bagging and random subspaces (denoted as BagSubspaces).  
Bagging or bootstrap aggregating [12], significantly improves the stability and accuracy of machine learning algorithms. In this technique, multiple versions of a model are trained on different subsets of the original dataset, which are selected with replacement.  
In the random subspaces method [18], also known as attribute bagging or feature bagging, each model in the ensemble is trained using a random subset of features rather than data points. This strategy reduces the correlation between the models within the ensemble by giving them different perspectives on the data based on different attributes.  
Random Forests, as described by [PERSON] [13], improve the bagging algorithm by adding an additional layer of randomness during tree construction. This method not only creates multiple bootstrap samples from the data but also selects a random subset of features at each decision point when the trees are constructed. Random forests can also evaluate the influence of each attribute on the predictions. In this process, numerous trees are trained on random subsets of data, and the impact of each attribute on the model's decision process is measured. This ranking identifies the most influential attributes in the dataset, providing valuable insights for attribute selection and model refinement.  
The method combines bagging and random subspaces, ref. [19] both the randomness of the data and the randomness of the features. In this innovative approach, multiple bootstrap samples are created from the original dataset, and each model is trained on a specific subset of features for each sample. By combining these methods, we aim to significantly increase the diversity of models in the ensemble, which can lead to better performance in terms of accuracy and robustness.