## 2 Methodology and Data  
Ensemble modelling, utilising machine learning algorithms, has emerged as a technique to enhance the accuracy and robustness of predictive models. This approach involves combining multiple models, often trained on different subsets of data or using different algorithms, to produce more accurate and reliable predictions. One of the main advantages of this approach is its ability to reduce the risk of model overfitting [35; 36]. Ensemble modelling can be classified into two main types: bagging and boosting. _Boosting_ entails training a sequence of models, where each subsequent model is trained to correct the errors of the previous model. On the other hand, _bagging_ involves training multiple models independently on different subsets of the training data [37; 38]. The individual predictions from these models are then combined through aggregation. The fundamental concept behind aggregation is to create multiple models that each provide a prediction on the same set of inputs. These individual predictions are then combined to generate a final prediction, resulting in a more accurate and robust prediction compared to that of any single model [35]. Various methods are available for combining models, and the choice of technique depends on the specific problem and the types of models being used. The most commonly used techniques for combining models include the following [35; 39; 40]:  
* _Stacking_: This method involves training multiple models and then using a meta-model to combine their predictions. The meta-model takes the predictions from each base model as inputs and learns to weigh them appropriately to produce the final prediction;
* _Competition_: In this method, individual predictions are considered, and the prediction with the highest confidence is selected for each case in the data;
* _Voting_: This method combines the individual models by taking the average of their predictions;
* _Weighted Voting_: Similar to the voting method, this approach also involves taking the average of the predictions from each model. However, the predictions are weighted based on a specific metric, such as confidence. Overall, combining models is a powerful technique for improving the accuracy, precision, and robustness of predictive models. By leveraging the strengths of multiple models, ensemble methods can often outperform any single model and provide more reliable predictions [41]. In this article, we aimed to create an ensemble model for predicting the unemployment duration of jobseekers in Slovakia. To achieve this, we employed the stacking method to combine the base models. The unemployment duration was quantified on a binned scale, commonly used in practice for unemployment evaluation (e.g., [42; 43]). We categorised the unemployment duration into the following categories: up to 3 months, 3-6 months, 6-12 months, and more than 12 months. Consequently, this prediction task falls under supervised machine learning, where the algorithm learns from labelled data with the known target variable. As part of the supervised learning machine learning techniques, we utilised _discriminant analysis_ and two decision tree types: the _Classification and Regression Tree_ (CART) and _Chi-squared Automatic Interaction Detection_ (CHAID). Each of these techniques possesses distinct advantages and weaknesses. Combining their predictions can produce more precise results than any individual model's prediction. The final model was created through _stacking_, where the individual predictions of the models mentioned above were used as inputs for the final meta-model. As the final meta-model, we employed _logistic regression_, with predictions based on the predictions of the individual models. We chose this method because it not only predicts the classification of the case into one of the categories of unemployment duration, but also predicts the probability for each category.