## 5 Conclusions  
In this study, we aimed to predict the duration of unemployment of jobseekers in Slovakia using an ensemble model created through the stacking method. Our approach involved developing three models using CART, CHAID, and discriminant analysis techniques. These models provided predictions for unemployment duration, and their outputs and confidence were used as input variables for a final meta-model created via logistic regression. If we were to examine the predictive ability of certain models in greater detail, the first model we developed was the CART model. Using a boosting technique, the CART model was constructed to improve its accuracy. Based on the macro- and micro-averages of the CART model evaluation statistics, we can conclude that the model's prediction accuracy exceeded 77%. This indicates that the given model accurately placed unemployed job seekers into one of the four categories of unemployment duration with an accuracy of greater than 77%. This model had the highest sensitivity (up to 75.84%) for identifying the category of long-term unemployed job seekers (those unemployed for more than 12 months). The CHAID model was the second model created. This model's accuracy in predictingthe duration of unemployment of jobseekers was slightly lower than the previous model, at approximately 70%. Thus, almost 70% of the time, the model's predictions regarding the length of unemployment for job seekers in one of the four categories were accurate. The CHAID model performed best in identifying the category of job seekers who had been unemployed for over a year, with a sensitivity of over 50%. The last model we developed was the discriminant model. This model's overall accuracy was greater than 66%. The average accuracy of the model's predictions regarding the duration of unemployment of job seekers in one of the four categories was 66%. With a sensitivity of over 64%, this model demonstrated the best performance in the category of unemployment duration of unemployed job seekers within three months. Our ensemble model for predicting unemployment duration achieved an overall accuracy of 77.60%. The final model classified unemployed job seekers into one of four groups of duration of unemployment: within three months, between three and six months, between six and twelve months, and more than twelve months. For the category of unemployment up to 3 months, the model's accuracy was nearly 42%; for the category of unemployment from 3 to 6 months, the model's accuracy exceeded 76%; and for the category of unemployment from 6 to 12 months, the model's accuracy was 70.54%. Notably, it exhibited exceptional performance in predicting the unemployment duration of jobseekers who remained unemployed for more than 12 months, with an accuracy of nearly 92% and a precision exceeding 91%. This indicates the potential of our model to identify those jobseekers threatened with long-term unemployment accurately. In addition, when examining the significance of the individual predictors of unemployment duration in the individual models, unemployment history, level of education, and age appear to be important. Our findings highlight the significance of a jobseeker's unemployment history in determining their future unemployment duration. This implies that prior unemployment experiences can serve as valuable indicators for forecasting the length of unemployment of jobseekers. The performance of our ensemble model emphasises the potential practical applications of our research. As the ensemble model was shown to be very accurate at predicting long-term unemployment (over 12 months), it has the potential to aid employment offices in identifying unemployed individuals who may be at a greater risk of long-term unemployment. By accurately predicting unemployment durations, policymakers and employment agencies can devise targeted interventions and support systems to assist jobseekers in finding employment more effectively.