# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 2 Background and Related Work
### Modelling Approach Selection  
Choosing the right modeling approach for an application scenario have a significant impact on the effectiveness and reliability of predictions. Building single models or ensemble models are two basic approaches that can be used for each of the tasks described above, with each approach having its strengths and challenges. **Single models** are generally easier to generate, interpret and implement. They can usually be trained faster than ensemble models and are therefore suitable for environments where speed is critical. These models require fewer computing resources, which can be a deciding factor when working with limited hardware or in cloud environments where computing time is costly. Single models are often more prone to overfitting, especially if they are not properly regularised or if the model is too complex in relation to the amount of training data. **Ensemble models** use multiple base models to achieve better predictive performance than could be achieved by one of the models alone. Examples of ensemble methods that can be used to generate ensemble models are Bagging [12], Random Forests [13], Random Subspaces [14] method and others. By combining multiple models, ensembles often achieve higher accuracy and better generalisation to unseen data compared to single models. They are particularly effective in reducing variance and bias. Ensemble methods are more robust against overfitting, especially in cases where the individual models are simple (e.g., decision trees in a random forest). In the context of this study, robustness refers to the ability of predictive models to provide consistent and reliable results across diverse and potentially noisy data sets and to maintain their performance in the face of various data irregularities and modeling challenges [15; 16]. Ensemble models are generally more complex to generate and tune due to the interactions between the individual models. The methods used to create them require more computing resources, which can increase training times and require more powerful hardware or cloud computing power. These models are usually more difficult to interpret than individual models. When predicting employee absenteeism, the distinction between single-target and multi-target prediction models can significantly influence the outcome and applicability of the predictions. These two approaches fulfill different prediction requirements and offer different advantages and challenges. **Single target prediction models** predict a dependent variable as output for each input instance. In the context of employee absence, this could mean predicting whether an employee will be absent on a given day (binary classification) or predicting the number of days an employee will be absent in a given week (regression). Single-target models can be fine-tuned to predict a particular outcome very well, which can be an advantage when high accuracy is required for that particular prediction. They do not take into account correlations between multiple targets, which can lead to inefficiencies and missed insights where outputs are interdependent. If predictions are needed for multiple targets (e.g., for the days of a week), a separate model is required for each target, which can complicate the use and maintenance of the prediction system. **Multi-target prediction models** simultaneously predict multiple dependent variables from the same set of inputs. When predicting employee absenteeism, this could mean predicting the number of days of absence over multiple weeks or predicting daily absence for each day of a single week. These models process multiple targets simultaneously, which can be more efficient in terms of calculation and data utilisation than creating a separate model for each target. They can take advantage of the inherent correlations between multiple targets, potentially improving the accuracy of all targets. By learning multiple targets at once, these models can generalise better, especially when the targets influence each other.