## 3 Results
### CART Model  
The CART model was constructed using a boosting technique to enhance model accuracy. By employing this method, the algorithm generated 10 component models, which were then utilised to train the boosted model. The individual models achieved accuracies (Formula (1)) ranging from 25 to 53.2%. The ensemble CART model achieved an accuracy of 53.1%. For comparison, the naive model, which randomly predicts one of the four categories of unemployment duration, has an accuracy of such random prediction of 25%. In this sense, the accuracy of the boosted ensemble CART model of 53.1% indicates that over half of the registrations of jobseekers were correctly classified into one of four categories of unemployment duration. We note that this accuracy pertains to the training set. To calculate the evaluation statistics (1)-(5) for the testing set, we computed the micro- and macro-average as explained in the methodology section. For a more comprehensive understanding of the jobseekers' correctly and incorrectly classified registrations in the testing set, we provide the coincidence matrix presented in Table 4.  
As mentioned above, to evaluate the sensitivity, specificity, precision, and F-measure of the model, we recalculated the confusion matrix into a 4-field matrix for each category of the target variable. These individual tables enable us to assess the performance of the model for each unemployment duration category. We present the cumulative 4-field confusion matrix in Table 5 and all values of the evaluation measures for the boosted CART are presented in Table 6. In the case of the evaluation measures, we do not only present the information for individual period categories of the length of the status \"unemployed\" (first four columns of Table 6), but also their macro- and micro-averages across the entire CART model (last two columns of Table 6). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Up to** & **3-6 Months** & **6-12 Months** & **More than** & **Macro-Average** & **Micro-Average** \\
**Measure** & **3 Months** & & **12 Months** & **Macro-Average** & **Micro-Average** \\ \hline
**sensitivity** & 69.17 & 22.97 & 44.62 & 75.84 & 53.15 & 54.82 \\
**specificity** & 67.37 & 94.79 & 79.91 & 98.06 & 85.03 & 84.94 \\
**precision** & 39.90 & 54.51 & 43.92 & 94.03 & 58.09 & 54.82 \\
**F-measure** & 50.61 & 32.32 & 44.27 & 83.96 & 52.79 & 54.82 \\
**accuracy** & 39.90 & 79.44 & 70.71 & 91.68 & 77.41 & 77.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation measures for boosted CART model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unemployment** & **Up to** & **3 Months** & **6-12 Months** & **More than** & **Current [\%]** \\
**Duration** & **3 Months** & **3-6 Months** & **6-12 Months** & **12 Months** & **Sum** & **Correct [\%]** \\ \hline
**up to 3 months** & 86,484 & 10,519 & 25,631 & 2391 & 125,025 & 0.692 \\
**3–6 months** & 54,710 & 25,734 & 29,698 & 1890 & 112,032 & 0.230 \\
**6–12 months** & 64,445 & 8266 & 60,991 & 2974 & 136,676 & 0.446 \\
**more than 12 months** & 11,136 & 2691 & 22,551 & 114,202 & 150,580 & 0.758 \\
**sum** & 216,775 & 47,210 & 138,871 & 121,457 & 524,313 & 0.548 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coincidence matrix of the boosted CART model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unemployment** & **Sum Correct** & **Sum Incorrect** & **Sum** & **Correct [\%]** \\ \hline
**sum correct** & 287,411 & 236,902 & 524,313 & **54.82** \\
**sum incorrect** & 236,902 & 1,336,037 & 1,572,939 & **84.94** \\
**sum** & 524,313 & 1,572,939 & 2,097,252 & **77.41** \\
**correct [\%]** & **54.82** & 15.06 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cumulative confusion matrix for boosted CART ensemble model. All of these measures were calculated for the testing set to determine the true quality of the model, unaffected by overfitting.