## 3 Results
### Best Suited Model Selection for Maize Grain Yield Prediction
The process is repeated until the desired number of features is achieved. Cross-validation is integrated into this process to optimize the number of features and prevent overfitting. Figure 8: Ensemble pipeline for our purposes. * Permutation importance: This technique gauges the importance of a feature by evaluating the decrease in a model's performance when the feature's values are randomly shuffled. A significant drop in performance indicates high feature importance. * LASSO regression coefficients: LASSO is a regression analysis method that uses L1 regularization. It has the capability to shrink some regression coefficients to zero, effectively selecting more significant features while eliminating the less impactful ones. * Correlation coefficient: This method calculates the linear correlation between each feature and the target variable. Features that have strong correlation coefficients are deemed important, as they have a substantial linear association with the target. * SHAP values: SHAP values furnish a metric for gauging the influence of each feature on the model's prediction. By attributing the difference between the prediction and the average prediction to each feature, SHAP provides a more intuitive comprehension of feature importance, particularly in the context of complex models. The study employed six different feature engineering techniques to prioritize the selection of features based on their importance, ensuring the utilization of the most significant predictors in the model. Tables 4 and 5 present the performance of various ML models across these distinct feature engineering methods. Among the top-performing models, the Huber model stands out, achieving the best \(R^{2}\) value of 0.926313 with features selected using SHAP values. This combination demonstrates minimal errors with an MAE of 0.278809, an MSE of 0.516539, and an RMSE of 0.718707, explaining approximately 92.63% of the variance in the dependent variable. The Huber model, when paired with features selected using the correlation coefficient, permutation importance, random forest importance, and RFE with CV, consistently maintains \(R^{2}\) values exceeding 92.63%, indicating its robust performance. The M estimators model, coupled with features selected using SHAP values and permutation importance, also showcases strong performance with an \(R^{2}\) value above 92.46%. Conversely, the top 10 worst performing models include the decision tree model with features selected using all features, permutation importance, and LASSO coefficients, exhibiting \(R^{2}\) values below 43%. This suggests potential overfitting to the training data or the decision tree model's limited suitability for the dataset. The passive aggressive model, using the correlation coefficient feature selection method, and the ElasticNet model, utilizing both correlation coefficient and LASSO coefficients, also present low \(R^{2}\) values, indicating weaker performance. The XGBoost model with features selected using SHAP values and the decision tree model with features selected using RFE with CV both have \(R^{2}\) values below 65%, further suggesting challenges with these combinations.