## 5 Discussion  
In terms of answering the research question regarding using imputation or not when 80% of the information for one feature is missing (considering that we have a total of 41 features as presented in Figure 11 from Appendix A), the preference would be towards the first option. Regardless of the method used for imputation, the model proved to be more accurate in all cases. On the other hand, given that we would lose 80% of the data, the risk is also to use other particularities in building the model. To emphasise this, in Figure 9 we analysed the yield value histogram when removing instances with missing planting day, compared to using all initial instances (which contain a second peak for the histogram distribution, suggesting perhaps a separate class cluster among the initial data, which seems to be representative, and not just outliers). From this figure we notice the need for imputation to preserve relevant data, otherwise some characteristics of the model are lost. When missing data instances are removed, the values between 0 and 500 for the yield are no longer present. However, they represent relevant data for some types of crops, even if they do not represent the majority of crop types, thus losing a component. Of course, one can argue here that an extra component could be  
Figure 8: \(MAE\) box plot values obtained with preprocessing techniques. The circles represent outliers. Figure 7: \(R^{2}\) box plot values obtained with preprocessing techniques. The circles represent outliers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **IF** & **LOF** & **MCD** & **OC-SVM** \\ \hline XGBT & \(R^{2}\) & \(p=3.78\) & \(p<0.01\) & \(p=2.55\) & \(p=1.36\) \\ \(MAE\) & \(p<0.01\) & \(p<0.01\) & \(p<0.05\) & \(p=2.19\) \\ \hline HGBR & \(R^{2}\) & \(p=2.96\) & \(p=5.23\) & \(p<0.01\) & \(p=9.88\) \\ \(MAE\) & \(p<0.01\) & \(p<0.01\) & \(p<0.03\) & \(p=5.14\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(p\) values for each outlier removal method versus none. approached by building separate models. For our case, we continue to consider them as a whole, part of the same model. To better visualise this, we rendered in Figure 10 a plot of the actual vs. predicted values, for the best result obtained, involving HGBR as the regression model, OC-SVM the unsupervised outlier removal method, and RF as the prediction imputation method. On the left, we have the plot obtained for the prediction model based on the data from which we removed instances with missing values. On the right, we have the model built using imputation, where we notice a linear trend involving two main clusters, one between 0 and 500 and one between 500 and 2500 for the yield value, in line with the histogram for the yield for the initial data. On the other hand, the left plot shows only one group, with the 0-500 yield value range missing. This shows that by removing data, we lose relevant information for the model, and the preprocessing methods involved, using imputation and outlier removal, are able to create a much more robust and reliable prediction model. For the research question on which imputation method produces the best model, prediction is preferred to all others in all cases, with RF algorithms producing the highest values for \(R^{2}\). The same trends in model improvement are present for both XGBT and HGBR, proving that the change in performance is mainly due to the preprocessing methods used, for both imputation and outliers removal. For the latter, concerning data anomalies and unsupervised outlier removal, further experiments would be required to establish if it can be more accurately fine-tuned and if higher contamination rates, such as \(c=0.2\), could be feasible to use in a larger dataset or if it would lead to model overfitting.