## 4 Experimental Setting  
This section presents the experimental results for Elastic Net Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, and Linear Regression to predict rice nutrient levels using FS one until six. Table 4 and Figure 4 display the RMSE scores of all six models, where Polynomial Regression has the best performance in four models to predict Ca%, K%, P%, and N%, with an average of 0.1502 RMSE, except in Model 2 (prediction of Mg%), with very little standard deviation (0.1980). According to Table 7, the optimal model for predicting Ca is ModelPN\({}_{\text{Ca}}\), demonstrating consistent performance across all evaluation metrics of R\({}^{2}\) Score, MAE, and RMSE. The bold highlighting in Table 7 indicates the significantly superior performance of the PN algorithm compared to other algorithms, emphasizing its effectiveness in capturing the variability of nutrient values. Two algorithms, EN and LS, could not capture the variability in the dataset for predicting Ca, based on the zero R\({}^{2}\) value. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **R\({}^{2}\) Score** & **MAE** & **RMSE** \\ \hline ModelEN\({}_{\text{Ca}}\) & 0.0 & 0.0297 & 0.0362 \\ ModelPN\({}_{\text{Ca}}\) & **0.5017** & **0.0204** & **0.0255** \\ ModelESW\({}_{\text{Ca}}\) & 0.0257 & 0.0292 & 0.0357 \\ ModelRR\({}_{\text{Ca}}\) & 0.0869 & 0.0281 & 0.0345 \\ ModelLS\({}_{\text{Ca}}\) & 0.0 & 0.0361 & 0.0297 \\ ModelLR\({}_{\text{Ca}}\) & 0.0931 & 0.0279 & 0.0345 \\ AVG & 0.1179 & 0.0286 & 0.0327 \\ STDEV & 0.1942 & 0.0050 & 0.0042 \\ \hline \end{tabular}
\end{table} 
Table 7: Performances of Ca prediction using approach 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **R\({}^{2}\) Score** & **MAE** & **RMSE** \\ \hline ModelEN\({}_{\text{Mg}}\) & 0.0 & 0.0154 & 0.0193 \\ ModelPN\({}_{\text{Mg}}\) & \(-\)3.1900 & 0.0301 & 0.0395 \\ ModelESW\({}_{\text{Mg}}\) & 0.0879 & 0.0151 & 0.0184 \\ ModelRR\({}_{\text{Mg}}\) & 0.1734 & 0.0142 & 0.0176 \\ ModelLSMg & 0.0 & 0.0154 & 0.01934 \\ ModelLR\({}_{\text{Mg}}\) & **0.1742** & **0.0141** & **0.0175** \\ AVG & \(-\)0.451 & 0.0174 & 0.0219 \\ STDEV & 1.3401 & 0.0063 & 0.0086 \\ \hline \end{tabular}
\end{table} 
Table 8: Performance of Mg prediction using approach 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **R\({}^{2}\) Score** & **MAE** & **RMSE** \\ \hline ModelEN\({}_{\text{K}}\) & 0.1967 & 0.3101 & 0.3991 \\ ModelPN\({}_{\text{K}}\) & **0.8496** & **0.1275** & **0.1726** \\ ModelESW\({}_{\text{K}}\) & 0.0926 & 0.3464 & 0.4241 \\ ModelLR\({}_{\text{K}}\) & 0.5873 & 0.2266 & 0.2860 \\ ModelLS\({}_{\text{K}}\) & 0.1391 & 0.3235 & 0.4131 \\ ModelLR\({}_{\text{K}}\) & 0.5895 & 0.2261 & 0.2852 \\ AVG & 0.4091 & 0.2600 & 0.3300 \\ STDEV & 0.3087 & 0.0823 & 0.0993 \\ \hline \end{tabular}
\end{table} 
Table 9: Performance of K prediction using approach 1. Contrary to its performance in Table 7, the PN algorithm shows a bad performance for magnesium. The best for magnesium prediction is the LR algorithm. The negative R\({}^{2}\) value of PN implies that the model is so inadequate that it is worse than a naive model that merely predicts the mean of the dependent variable for all observations. This indicates that PN could have been overfit and too complex for the given data, and it fits noise rather than the underlying patterns. The performances of LR and RR are very similar, which reflects their high similarity. Both algorithms assume a linear relationship between the independent variables and the dependent variable. The models are expressed as linear combinations of the input features. Both methods aim to minimize a certain objective function to find the optimal set of coefficients that best fits the data. In LR, this is typically done by minimizing the sum of the squared differences between the predicted and actual values. In RR, the objective function includes an additional regularization term. The primary difference between RR and LR lies in how they handle multicollinearity and overfitting.