## 3 Results
### Regressions Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Ingredient** & **Description** & **References** \\ \hline \multirow{5}{*}{Citric acid} & Gum Bhatti & The addition of gum ghatti in biodegradable sodium alginate edible films & [50] \\  & & increased the tensile strength. & \\ \cline{2-4}  & Citric acid & The addition of citric acid significantly decreased the TS of the casing of alginate films. & [82; 83] \\ \cline{2-4}  & Soybean oil & The tensile strength decreased with increasing oil concentrations due to the plasticizing effect from oil of alginate films. & [84] \\ \cline{2-4}  & PolyethyleneGlycol (PEG) & PEG is used as a plasticizer, improving the mechanical properties of bioplastic film from seaweeds. & [85; 86] \\ \cline{2-4}  & Shikonin & Shikonin is used as a reinforcement. The gelatin/carrageenanan film’s mechanical properties did not change significantly by shikonin. But the incorporation into carboxymethyl cellulose/agar films slightly improved tensile strength, showing a reinforcing effect. & [49; 87] \\ \cline{2-4}  & Anthocyanin & Addition of tensile anthocyanin showed a plasticizing effect in polyvinylidene fluoride films. However, Kadsura occinea extract added to a chitosan, gelatin, and sodium alginate film increased tensile strength. & [88; 89] \\ \cline{2-4}  & \multirow{2}{*}{Starch extract} & A decrease in tensile strength was observed in starch/agar composite films. & [19; 47] \\ \cline{2-4}  & Barbatimao extract (_Stryphandendron_ & The incorporation of _Stryphandendron_ _astringens_ extract improved mechanical properties of gelatin membranes. & [90] \\ \cline{2-4}  & \multirow{2}{*}{Cellulose extract} & The chitosan-sodium alginate-ethyl cellulose polyelectrolyte films & [91] \\ \cline{2-4}  & & showed high tensile strength. & \\ \hline \multirow{2}{*}{Cottonni extract} & \multirow{2}{*}{_Eucheum cottonii_ extract was incorporated as a biofilier to improve tensile strength values of starch/agar composite films.} & [19] \\ \cline{2-4}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)is more accurate and less affected by extreme values when predicting tensile strength. Furthermore, if we look at the \(RMSLE\), which is sensitive to the scale of the target variable, we find that although the values are comparable, they are still slightly lower for \"Tensile strength\" (0.468 for random forest) compared to \"Elongation at break\" (0.474 for random forest). This suggests that the model is not only more accurate in absolute terms, but that it also maintains this accuracy over different scales of the target variable. Therefore, a detailed comparison of errors between the predictor variables reveals that the models perform considerably better in predicting tensile strength, both in terms of error magnitude and robustness to outliers and scale variability of the target variable. This observation underscores the importance of data quality in developing and evaluating regression models in sparse data contexts. It is essential to note that a higher test \(R^{2}\) generally suggests a more accurate model, but its interpretation becomes more nuanced when considered in conjunction with other error metrics. In the case of tensile strength, random forest has the highest test \(R^{2}\) (0.821), and this correlates well with lower errors in all metrics (\(MSE\) of 64.310, \(RMSE\) of 8.019, \(MAE\) of 5.003, \(MedAE\) of 2.632, and \(RMSLE\) of 0.468). Here, the test \(R^{2}\) and error metrics show a consistent proportional relationship, where higher \(R^{2}\) means lower errors. However, this relationship is not uniform across all predictor variables nor across all models. For example, in the case of \"Elongation at break\", random forest has a test \(R^{2}\) of 0.421, which is significantly higher than the \(R^{2}\) of gradient boosting, which is 0.156. Despite this large difference in \(R^{2}\), the \(MedAE\) for gradient boosting is 3.881, which is lower than the \(MedAE\) of 4.590 for random forest.