## 3 Results and Discussion
### Ripeness Classification Results  
#### 3.2.1 ML Models' Accuracy  
Table 4 presents the performance analysis of different ML models for the classification of the samples. LDA had a relatively good performance with R\({}^{2}\): 0.48, RMSE: 0.46, MAE: 0.40. The smaller value of R\({}^{2}\) with relatively higher error measures shows the poor capability of LDA in dealing with nonlinear separability between kiwifruit classes. Being a linear model, it assumes normally distributed features and equal covariance between classes, which may not be the case for this dataset. This is what causes the model to poorly classify samples, particularly when the class boundaries are not well-defined.  
ANN model ranked as the best model with the best R\({}^{2}\) of 0.95, the least RMSE of 0.08, and the minimum MAE of 0.03 among the other models. These findings demonstrate great model generalization and accurate detection of the right class of kiwifruit. The better performance of the network may be attributed to its capacity for modeling complex nonlinear relationships and learning from higher-dimensional data. The low value of error rates and high value of determination coefficient show that the ANN model does well with noise and overlapping features, which is the best model for this classification task.  
We compared the results for three DT techniques: J48, CART, and LMT. The best performance was achieved by LMT with: R\({}^{2}\): 0.50, RMSE: 0.45, and MAE: 0.38. J48 and CART produced a little lower R\({}^{2}\) values (0.48) and slightly higher errors, especially in MAE (J48: 0.41, CART: 0.42). These results indicate that Decision Trees can model patterns in structured data; their performance might be inhibited based on learning style hyperparameters that either learns overly-simplified concepts (underfit) or have trouble generalizing the target function (overfit). Since LMT fuses logistic regression and the structure of tree-based learning, it is possible to have a better generalization and error rate than of the others.  
With the SVM model, four kernel functions were compared. For kernels, the RBF function presented the superior performance with the highest R\({}^{2}\) and the smallest RMSE and MAE. This demonstrates that the RBF network does well with nonlinear associations. Excise and Pearson, as well as Polynomial kernels, presented a slightly lower performance, which means they could not adapt themselves well to the complexity of the data. However, in general, SVM models provided a better trade-off between accuracy and error when compared to LDA and standard Decision Tree approaches.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Model** & **LDA** & **ANN** & **DTs** & & & \multicolumn{3}{c}{**SVM**} & \\ \hline Method & & & J48 & CART & LMT & RBF & Polynomial & Gaussian & Pearson \\ Performance & & & & & & & & & \\ R2 & 0.48 & 0.95 & 0.48 & 0.50 & 0.48 & 0.65 & 0.59 & 0.61 & 0.57 \\ RMSE & 0.46 & 0.08 & 0.46 & 0.45 & 0.47 & 0.35 & 0.39 & 0.38 & 0.42 \\ MAE & 0.40 & 0.03 & 0.41 & 0.38 & 0.42 & 0.28 & 0.31 & 0.29 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of ML models and their respective methods or kernel functions used for classifying three classes of kiwifruit.