## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model Parameters**} & \multicolumn{3}{c}{**ML Algorithm**} \\ \cline{2-4}  & **Decision Trees** & **Random Forests** & **XGBoost** \\ \hline Criterion to measure the quality of a split & entropy & entropy & - \\ Maximum depth of the tree & 3 & 5 & 4 \\ Minimum number of samples required to be at a leaf node & 1 & 1 & - \\ Minimum number of samples required to split an internal node & 2 & 2 & - \\ Number of features to consider when looking for the best split & - & log2 & - \\ Number of trees in the forest & - & 20 & - \\ Number of boosting stages & - & - & 50 \\ Learning rate & - & - & 0.1 \\ Minimum sum of instance weight (hessian) needed in a child & - & - & 0 \\ Minimum loss reduction required to make a further partition on a & - & - & 0 \\ leaf node of the tree & - & - & 0 \\ Fraction of samples to be used for fitting the individual base & - & - & 0.8 \\ learners & - & - & 0.55 \\ Subsample ratio of columns when constructing each tree & - & - & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated parameters obtained by grid search for each machine learning algorithm. Figure 3: Boxplots of classification evaluation metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Learning** \\ **Algorithm** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Test Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Balanced Test** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Test F1-Score** \\ \end{tabular} \\ \hline Decision trees & 0.99 \(\pm\) 0.005 & 0.93 \(\pm\) 0.06 & 0.88 \(\pm\) 0.12 & 0.93 \(\pm\) 0.06 \\ Random forests & 1.00 \(\pm\) 0.002 & 0.96 \(\pm\) 0.05 & 0.94 \(\pm\) 0.09 & 0.96 \(\pm\) 0.05 \\ XGBoost & 1.00 \(\pm\) 0.0 & 0.96 \(\pm\) 0.05 & 0.92 \(\pm\) 0.10 & 0.96 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics and scores for each learning algorithm. Values are mean \(\pm\) standard deviation. Coincidently, test F1 scores are the same as test accuracy scores. We have applied the Wilcoxon rank sum statistical test with significance threshold at 0.05 and Bonferroni-Holm correction for multiple testing. All the pairwise comparisons between the three ML methods revealed statistically significant differences, regardless of the evaluation metric considered. In addition to the evaluation scores, model behavior can also be assessed in more detail through confusion matrices. These provide more information regarding class attribution of each sample by the classifier and report on the frequency of the misclassifications. Figure 4 shows the confusion matrices computed for the selected models in each algorithm. In accordance with the model evaluation results, RF and DT are the algorithms with the lowest (6%) and highest (12%) misclassification rates per class, respectively. The matrices indicate that all algorithms are able to correctly distinguish severely water-stressed plants (S2) from the well-watered controls (S0) but moderately-stressed plants (S1) are mislabeled as controls (and vice versa) more often than with S2. In comparison, S2 is more often misclassified as S1, especially in DT. Due to their simplicity and ease of interpretation, in Figure 5 we illustrate a decision tree of a DT classifier, obtained from the model random runs. The tree shows the binary decisions at each split as if/then rules, based on the features chosen by the model in order to place subsets of the data in different nodes, according to their class label. Figure 4: Average confusion matrices obtained from all test folds across multiple runs of the best cross-validated model with one thousand different seeds in each machine learning algorithm. The blue colors represent a heatmap of the predicted class against the actual class in the confusion matrices, where darker shades indicate larger values and lighter shades represent smaller values. Figure 5: Decision tree to classify the SWC in _A.