## 2 Methodology and Data
### Evaluation of Results  
To evaluate the results obtained, we employed various evaluation statistics. To ensure the relevance of evaluating the quality of the created ensemble model, we divided the dataset into a training part, on which the machine learning models were created, and a testing part, which served for the independent evaluation of the predictions. Therefore, predictions of unemployment duration were generated for all units in the dataset, including those in the testing set. Predictions of registrations in the testing set were made using an ensemble model that had not been trained, which allowed us an independent assessment of the quality of the model by comparing these predictions to actual outcomes. In this study, we only present the evaluation results for the testing set. Given enough cases in the dataset, the training and testing parts of the sample were allocated in a random 50:50 ratio. We utilised the statistics mentioned in the methodology section to evaluate the individual results. The entire ensemble model was initially evaluated using a confusion (or coincidence) matrix and its associated statistics, i.e., let \(TP\) represent the number of true positive cases; \(FP\) the number of false positive cases; \(TN\) the number of true negative cases; and \(FN\) the number of false negative cases, where the \"hit\" is the importance value of the target variable. The overall model evaluation was based on its _accuracy_, which measures the proportion of correctly classified cases from the entire testing sample [60]:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN}. \tag{1}\]  
For a more detailed view, we also report the following [60]:  
* _sensitivity_--the row % of true positive predictions, calculated as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Duration of Registration** & **Proportion of the Sample** &
\begin{tabular}{c} **Multiplication Factor for** \\ **Balancing via Boosting** \\ \end{tabular} \\ \hline up to 3 months & 23.85\% & 1.2036 \\
3–6 months & 21.34\% & 1.3453 \\
6–12 months & 26.10\% & 1.0998 \\ more than 12 months & 28.71\% & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of target variable “duration of registration”, i.e., the status “unemployed”. \[Sen=\frac{TP}{TP+FN} \tag{2}\]
* _specificity_--the row % of true negative predictions, calculated as follows: \[Spec=\frac{TN}{FP+TN}\] (3)
* _precision_--the column % of true positive predictions, calculated as follows: \[P=\frac{TP}{TP+FP}\] (4)
* _F-measure_--the harmonic mean of the accuracy and sensitivity, calculated as follows: \[F=\frac{2 TP}{2 TP+FP+FN}.\] (5)  
Since the target variable consists of four possible values instead of being binary, the calculation of these evaluation measures must be adjusted. The general coincidence matrix cannot be used directly and thereby we employed the _macro-average_ and _micro-average_ methods. These approaches involve converting the general coincidence matrix into a 4-field matrix. We performed this conversion for each category of the target variable, comparing the prediction for each category against the summed predictions for all other categories. The evaluation measures (1)-(5) were then calculated based on the resulting 4-field tables, with either the average (macro-average) or by summing partial 4-field tables and calculating the evaluation measures from the resulting combined table (micro-average). To assess the importance of the individual input variables in the individual models, we calculated the predictor importance. This analysis aims to highlight the characteristics of an individual that have the greatest impact on the duration of jobseekers' unemployment. All calculations were performed using IBM SPSS Modeler 18.3, a data-mining software. We approached this problem as a data-mining task, working with large datasets (big data) that represent the entire population of registered jobseekers during the monitored period rather than a random sample from the population.