# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 3 Results  
### Comparison of Machine Learning Performance  
As elaborated in Section 2.3.2, for each ML model hyperparameter, tuning took place through GridSearchCV, guaranteeing the best possible performance configurations. Towards assessing the predictive capability of the developed ML models, namely DT, XGBoost, RF, KNN, SVR, and MLPR, a comparative analysis was implemented using key evaluation metrics. Figure 3a-f depict scatter plots that compare the predicted crop yields against the actual values regarding the six ML models on the test dataset to illustrate generalization performance. Among them, XGBoost succeeded the highest \(R^{2}\) score (0.8756), demonstrating the strongest predictive performance, followed by MLPR (\(R^{2}=0.8141\)), and SVR (\(R^{2}=0.7933\)). This ML regressors' exceptional performance can be ascribed to their capacity to effectively capture non-linear correlations in high-dimensional data. Specifically, XGBoost outperformed the other models, because of its ensemble boosting framework that creates a powerful predictive model by combining several weak learners. It also integrates regularization to avoid overfitting and is optimized for both accuracy and speed [69].  
In addition, MLPR, exploiting its multi-layer architecture is able to capture complex patterns in the data [70], which is particularly beneficial in this analysis where interactions  
Figure 3: Scatter plots showing yield predictions on the lupin test dataset by (**a**) Decision Tree; (**b**) XGBoost; (**c**) Random Forest; (**d**) Support Vector; (**e**) K-Nearest Neighbors; and (**f**) Multi-Layer Perceptron Regressor models.  
among spectral bands and VIs are often non-linear [20]. As far as SVR is concerned, it maps input features into higher-dimensional spaces using kernel functions. This enables SVR to model complex relationships in the data, while emphasizing minimizing generalization error. This renders it resilient to overfitting. On the other hand, more scattered predictions were observed for KNN (\(R^{2}=0.6987\)) and RF (\(R^{2}=0.6959\)), with DT exhibiting the lowest predictive performance (\(R^{2}=0.6393\)). The poorest performance of DT can be attributed to the restrictions of single decision trees that lack generalization capability and have propensity to overfit training data [71].  
For each of the six ML models, the evaluation metrics are summarized in Table 1. Note that for \(MAE\), \(MSE\), and \(RMS\), lower values indicate better performance, whereas for \(R^{2}\), higher values indicate better performance. With the lowest \(MAE\) (0.2399), low \(MSE\) (0.1149), and \(RMSE\) (0.3389) as well as the highest \(R^{2}\) score mentioned above, XGBoost performed better than the other regressors. These values demonstrate its great capability to effectively estimate the relationship between remote sensing-related variables and crop yield. With \(R^{2}\) values approximately equal to 0.8, both MLPR and SVR showed competitive performance, signifying their capacity to capture nonlinear interactions. KNN and RF performed moderately well, but their lower \(R^{2}\) values (\(\sim\)0.7) showed that their predictions were less accurate than those of XGBoost. Finally, the DT model produced the weakest results on all metrics, indicating the limitations of its single-tree structure.