## 4 Discussion
### Research Limitations  
In our research one can find some research limitations. The first research limitation is the subjectivity of our results. Even though random forest models are considered to be more accurate models than linear regression, in our case linear regression models performed more accurate predictions on the final test set than random forest or decision tree models. Even though this result is not usual, we believe this could be for two reasons. The first reason may be a coincidence--linear regression models were simply lucky and better captured data from the specific test set. As in this case there was only one test set, there is some probability that linear regression could achieve higher accuracy on this set. The second reason may be that the final test set contained data that could be modelled by a linear function. Since linear regression is a linear model, it could model linear data better than decision tree or random forest models. However, to avoid the subjectivity of our results, in addition to standard ex-post testing, we also performed five-fold-cross validation. The reason for that was a higher objectivity of our results. In this procedure we divided training data into five folds, created five datasets for training and five datasets for validation of our models. To obtain more objective results we then calculated the average error on the validation set. In this case the results of our models in the final test set changed compared to the results from the ex-post testing. While the random forest and decision tree models achieved the best results in cross-validation, it was not the case in the final test. This means that the decision tree and the random forest models are better in general, but this may not be the case for a specific test set that has been randomly selected from all data. In this one case, the linear regression models achieved better results. Nevertheless, based on higher objectivity, we would recommend choosing a random forest as the default model for studying average predictions for different datasets. Its generalization ability was better than that of linear regression and decision tree models. The second limitation is the method selection. We are aware of the fact that there are many more methods for regression problems in machine learning. However, we tried to use only some of them. We selected some basic methods (linear regression, decision trees) as well as more advanced methods (random forest). There is chance that if we selected other supervised machine learning methods, we could achieve higher accuracy. For example, we could use deep neural networks and support vector machines or conditional random field models. However, we believe that even with our selected methods the accuracy was relatively high, and the models were able to model the GPA. The width of our data (number of variables) is the third limitation. Even though we collected quite a number of variables from our respondents, we believe there are other variables (factors) which influence the GPA significantly. Unfortunately, it is not possible to collect all possible data from respondents. However, we believe that by obtaining additional information from students we could improve our models and make the prediction of the average more accurate. For example, these new variables could include grades from specific school-leaving subjects, the success rate percentage in the written part of the school-leaving examination or grades from several subjects that the student has successfully completed at the faculty. The fourth limitation of our research is the size of our dataset (number of observations). Our sample consisted of answers of 79 respondents (university students). It is obvious that the results would not be the same if our dataset was different. However, it is questionable whether the increased size of our dataset would change our results. We tried to generalize our findings on the whole population by using statistical hypothesis testing and using more validation sets using cross-validation procedure.