## 3 Results
### Discriminant Model  
The discriminant analysis model was constructed as a boosted model for increasing the accuracy of the model and produced predictions of the discriminant score for each officially registered jobseeker. According to this score, a jobseeker is categorised into one of four categories of unemployment duration. For the quality of the model, it was important to assess the discriminant ability of the input variables that were later used in the process of the model's creation for selecting important variables. It can be checked using the values of the canonical discriminant function coefficients in the standardised form suitable for their comparison. These coefficients are presented in Table 10, together with the correlations between the discriminating variables and discriminant functions (in parentheses). Figure 2: Frequency of using predictors in the component models of the boosted CHAID model. Among these variables, the cumulative unemployment history (in the figure, regarded as _cumulative_) is the best discriminatory variable for the shortest unemployment duration. The best discriminatory variable for 3-6 and 6-12 months of unemployment duration is the number of days from the last occupation to the current registration. This variable is the most important predictor in the whole discriminant model. Overall, according to the discriminant analysis result, unemployment history is very important in the future unemployment duration of jobseekers. Moreover, a jobseeker's age is also very important. The coincidence matrix of the discriminant model is shown in Table 11. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Up to 3 Months** & **3–6 Months** & **6–12 Months** \\ \hline age & \(-0.086\) & 0.612 & 0.149 \\ (\(-0.135\)) & (0.723) & (\(-0.062\)) \\ \hline work before & 0.289 & 0.488 & \(-0.718\) \\ registration & (0.113) & (0.708) & (\(-0.562\)) \\ \hline cumulative previous & 0.734 & 0.298 & 0.610 \\ registrations & (0.723) & (0.239) & (0.612) \\ \hline average & \(-0.692\) & 0.306 & 0.552 \\ unemployment & (\(-0.614\)) & (0.460) & (0.422) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Standardised canonical discriminant function coefficients, and their correlation with discriminant functions. Figure 3: Predictor importance in the discriminant model. The cumulative 4-field confusion matrix for this model, recalculated to calculate the averaged evaluation statistics, is presented in Table 12.  
The evaluation measures for each category of unemployment duration and their macro- and micro-averages for the entire discriminant model are presented in Table 13.  
The evaluation measures listed in Table 13 were calculated for the testing set, thus depicting the real quality of the model. The discriminant model demonstrated its best performance in the category of jobseekers unemployed for up to 3 months, with a sensitivity of over 64%. The highest accuracy of 75.3% was achieved in the group of jobseekers with unemployment lasting from 3 to 6 months. On the other hand, the model achieved a weaker accuracy in the category of jobseekers unemployed for up to 3 months. The overall accuracy of the discriminant model was more than 66%, i.e., on average, the correctness of the discriminant model in the prediction of the unemployment duration of jobseekers by clustering them into one of four time categories achieved 66% accuracy. The sensitivity of this model was almost 33% according to the macro- and micro-averages. The discriminant model can be described as a set of discriminant functions for all categories of unemployment duration ([PERSON]'s discriminant functions). Their coefficients are presented in Table 14. A jobseeker is then predicted to be unemployed for a period with the highest value of discriminant functions.