## 4 Results  
Table 3 shows the comparison between several metrics obtained by the selected algorithms, when using each of the five approaches (presented in the Section 3) in dealing with missing data regarding the planting date. In our case, we are dealing with 984 instances with 41 attributes, of which 80% of the instances are only missing the planting date attribute, and was completed using imputation methods. In this table, we also included the evaluation of the model for the case when the instances with missing data were removed (\(n=312\)). The mean yield value is 1093.053, meaning the \(MAE\) of 87.628 represents a percentile error of \(MAE\%\) = 8.01%, and all \(MAE\) values under 100 have a corresponding \(MAE\%\) under 10%. We notice that the \(R^{2}\) values increase when imputation is used as opposed to removing incomplete data instances: from 0.723 for XBGT and 0.740 for HGBR to values in the range of 0.918-0.929 for the different imputation utilised methods. Accordingly, the \(MAE\) was reduced from 111.645 for XGBT (remove missing) to 81.483-91.202, meaning a reduction in \(MAE\%\) from 10.2% to 7.44% in the best case. Therefore, the first important result is that the imputation step greatly improves our model, regardless of the approach used. However, it can still be further fine-tuned and improved. The next step was to handle outliers in our data. Tables 4-7 show the results obtained after using each of the four outlier removal methods. The notations used in these tables are as explained in Table 3. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**XGBT**} & \multicolumn{2}{c}{**HGBR**} \\ \cline{2-5}
**Method** & \(\mathbf{R^{2}}\) & **MAE** & \(\mathbf{R^{2}}\) & **MAE** \\ \hline Remove missing & 0.743 (0.167) & 106.242 (19.641) & 0.752 (0.163) & 102.309 (17.622) \\ Similar fill & 0.928 (0.019) & 88.156 (10.262) & 0.927 (0.024) & 86.301 (10.678) \\ Mean values & 0.928 (0.022) & 85.868 (10.136) & 0.930 (0.022) & 83.769 (10.009) \\ Predict (LR) & 0.930 (0.020) & 85.632 (10.274) & 0.930 (0.021) & 85.126 (9.833) \\ Predict (HGBR) & 0.933 (0.021) & 82.086 (7.851) & 0.934 (0.020) & 81.156 (7.867) \\ Predict (XGBT) & 0.937 (0.017) & 81.038 (9.599) & 0.936 (0.018) & 81.159 (9.896) \\ Predict (RF) & 0.937 (0.018) & 80.106 (9.159) & 0.934 (0.019) & 81.414 (8.320) \\ Interpolation (L) & 0.927 (0.022) & 88.607 (10.667) & 0.930 (0.021) & 86.025 (9.692) \\ Interpolation (P) & 0.928 (0.020) & 86.998 (9.626) & 0.929 (0.020) & 85.743 (9.561) \\ Interpolation (S) & 0.932 (0.042) & 80.357 (13.635) & 0.932 (0.041) & 80.123 (13.238) \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative results of using different preprocessing methods for missing values, after removing outliers with Isolation Forest. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**XGBT**} & \multicolumn{2}{c}{**HGBR**} \\ \cline{2-5}
**Method** & \(\mathbf{R^{2}}\) & **MAE** & \(\mathbf{R^{2}}\) & **MAE** \\ \hline Remove missing & 0.727 (0.130) & 108.730 (17.376) & 0.753 (0.118) & 103.766 (16.987) \\ Similar fill & 0.925 (0.019) & 88.245 (9.865) & 0.928 (0.017) & 85.205 (9.155) \\ Mean values & 0.927 (0.022) & 87.068 (10.362) & 0.929 (0.022) & 84.388 (10.372) \\ Predict (LR) & 0.926 (0.022) & 86.516 (10.893) & 0.928 (0.023) & 84.766 (11.757) \\ Predict (HGBR) & 0.930 (0.024) & 84.503 (10.754) & 0.931 (0.023) & 84.027 (10.103) \\ Predict (XGBT) & 0.931 (0.032) & 81.576 (10.299) & 0.934 (0.030) & 79.629 (10.327) \\ Predict (RF) & 0.934 (0.022) & 78.954 (10.882) & 0.936 (0.021) & 78.223 (11.116) \\ Interpolation (L) & 0.921 (0.018) & 91.107 (9.300) & 0.925 (0.018) & 87.509 (8.895) \\ Interpolation (P) & 0.925 (0.019) & 88.245 (9.865) & 0.928 (0.017) & 85.205 (9.155) \\ Interpolation (S) & 0.921 (0.018) & 91.107 (9.300) & 0.925 (0.018) & 87.509 (8.895) \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative results of using different preprocessing methods for missing values, after removing outliers with local outlier factor.