## 3 Results
### Best Suited Model Selection for Maize Grain Yield Prediction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Set** & **Model** & **MAE** & **MSE** & **RMSE** & \(R^{2}\) \\ \hline SHAP Values & Huber & 0.278809 & 0.516539 & 0.718707 & 0.926313 \\ Correlation Coefficient & Huber & 0.278873 & 0.516555 & 0.718717 & 0.926311 \\ Permutation Importance & Huber & 0.279073 & 0.516604 & 0.718752 & 0.926304 \\ Random Forest Importance & Huber & 0.279069 & 0.516617 & 0.718760 & 0.926302 \\ RFE with CV & Huber & 0.279063 & 0.516617 & 0.718761 & 0.926302 \\ All Features & Huber & 0.279325 & 0.517176 & 0.719150 & 0.926222 \\ SHAP Values & M estimators & 0.299684 & 0.526963 & 0.725922 & 0.924826 \\ Permutation Importance & M estimators & 0.303802 & 0.528391 & 0.726905 & 0.924622 \\ Random Forest Importance & M estimators & 0.303904 & 0.528540 & 0.727007 & 0.924601 \\ RFE with CV & M estimators & 0.304337 & 0.529960 & 0.727983 & 0.924398 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 10 best performances of fourteen ML model across six distinct methods of feature engineering. In summary, the results highlight that the Huber model consistently demonstrates strong performance across multiple feature-selection methods, indicating its suitability for this dataset and problem. Conversely, the decision tree model appears less suited, producing some of the lowest \(R^{2}\) values, suggesting it might not be the optimal choice or may require further refinement through hyperparameter tuning or constraints to prevent overfitting. The study also underscores the effectiveness of feature-selection methods such as SHAP values, correlation coefficient, permutation importance, random forest importance, and RFE with CV, particularly when combined with the Huber model. Consequently, Huber, M estimators, ridge regression, and linear regression were selected from six different feature importance techniques for further processing with the hyperparameter tuning technique--an essential step in the ML workflow. The primary objective was to search for the optimal combination of hyperparameters that yields the best model performance. The GridSearchCV method facilitates an exhaustive search over a specified parameter grid, employing cross-validation to estimate the performance of each combination. For the Huber model, the best parameter tuning resulted in an achieved \(R^{2}\) of 0.926266, setting the regularization strength (\(\alpha\)) to 1, the Huber threshold (\(\epsilon\)) to 1.0, and the maximum number of iterations (max_iter) to 100. For the parameters of M estimators within the RANSACRegressor algorithm, the minimum number of samples (min_samples) was set to 0.7 of randomly available data samples for each iteration, and the stop-iterating probability (stop_probability) was set to 0.9. The obtained \(R^{2}\) of 0.924029 was achieved when 70% of the samples were used to fit the model in each iteration, and the algorithm iterated until there was a 96% probability of correctly identifying the inliers. The linear regression model, configured with fit_intercept=True, enhances flexibility, allowing it to better capture data patterns, resulting in an achieved \(R^{2}\) of 0.918301. Fine-tuning the parameters of the ridge regression model involved setting the alpha to 0.615848211066026, fit_intercept to True, and selecting \"lsqr\" as the solver. The \"lsqr\" solver, representing \"Least Squares QR Decomposition\", proves efficient in solving the least squares problem. The ridge regression model yielded the best \(R^{2}\) score of 0.916892 when the regularization strength was 0.615848211066026, an intercept was included, and the lsqr solver computed the coefficients. A summarized overview of the hyperparameter tuning results is presented in Table 6.