## 3 Results
\begin{table}
\begin{tabular}{c c|c c c|c c c|c c c|c c|c} \hline \hline  & & \multicolumn{3}{c|}{**Precision**} & \multicolumn{3}{c|}{**Recall**} & \multicolumn{3}{c|}{**F1**} & \multicolumn{3}{c|}{**Accuracy**} & \\ \hline
**Model** & **Threshold** & **0.25** & **0.50** & **0.75** & **0.25** & **0.50** & **0.75** & **0.25** & **0.50** & **0.75** & **0.25** & **0.50** & **0.75** & **Support** \\ \hline \multirow{4}{*}{1} & No overtop. & 1.00 & 0.99 & 0.99 & 0.98 & 1.00 & 1.00 & 0.99 & 1.00 & 0.99 & & & & 6656 \\  & Overtop. & 0.35 & 0.86 & 0.00 & 0.99 & 0.36 & 0.00 & 0.52 & 0.50 & 0.00 & & & 87 \\  & Macro avg & 0.67 & 0.93 & 0.49 & 0.98 & 0.68 & 0.50 & 0.75 & 0.75 & 0.50 & 0.98 & 0.99 & 0.99 & 0.6743 \\  & Weight. avg & 0.98 & 0.99 & 0.97 & 0.98 & 0.99 & 0.99 & 0.98 & 0.99 & 0.98 & & & 6743 \\ \hline \multirow{4}{*}{2} & No overtop. & 1.00 & 0.99 & 0.99 & 0.99 & 1.00 & 1.00 & 0.99 & 0.99 & & & & 6656 \\  & Overtop. & 0.47 & 0.00 & 0.00 & 0.99 & 0.00 & 0.00 & 0.64 & 0.00 & 0.00 & & & 87 \\  & Macro avg & 0.73 & 0.49 & 0.49 & 0.99 & 0.50 & 0.50 & 0.81 & 0.50 & 0.50 & 0.99 & 0.99 & 0.99 & 0.99 \\  & Weight. avg & 0.99 & 0.97 & 0.97 & 0.99 & 0.99 & 0.99 & 0.99 & 0.98 & 0.98 & & & 6743 \\ \hline \multirow{4}{*}{3} & No overtop. & 1.00 & 0.99 & 0.99 & 0.99 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 & & & 11,131 \\  & Overtop. & 0.58 & 0.91 & 1.00 & 0.87 & 0.62 & 0.43 & 0.70 & 0.74 & 0.60 & & & 168 \\ \cline{1-1}  & Macro avg & 0.79 & 0.95 & 1.00 & 0.93 & 0.81 & 0.71 & 0.85 & 0.87 & 0.80 & 0.99 & 0.99 & 0.99 & 11,299 \\ \cline{1-1}  & Weight. avg & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & & & & 11,299 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification report for the three wave overtopping models using a 0.25, 0.5, and 0.75 threshold decision boundary on the training data predictions. \begin{table}
\begin{tabular}{c c c c|c c|c c} \hline \hline  & \multicolumn{3}{c|}{**Predicted**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Threshold**} & \multicolumn{1}{c|}{**0.25**} & \multicolumn{1}{c|}{**0.5**} & \multicolumn{1}{c}{**0.75**} \\ \hline
**Model** & **Real Class** & **No Overtop.** & **Overtop.** & **No Overtop.** & **Overtop.** & **No Overtop.** & **Overtop.** \\ \hline \multirow{2}{*}{1} & No overtop. & 6496 & 160 & 6,651 & 5 & 6656 & 0 \\  & Overtop. & 1 & 86 & 56 & 31 & 87 & 0 \\ \hline \multirow{2}{*}{2} & No overtop. & 6559 & 97 & 6651 & 0 & 6656 & 0 \\  & Overtop. & 1 & 86 & 87 & 31 & 87 & 0 \\ \hline \multirow{2}{*}{3} & No overtop. & 11,027 & 104 & 11,120 & 11 & 11,131 & 0 \\  & Overtop. & 22 & 146 & 63 & 105 & 96 & 72 \\ \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices for the three wave overtopping models using a 0.25, 0.5, and 0.75 threshold decision boundary on the training data predictions. Table 5 shows the confusion matrices for the three wave overtopping models obtained evaluating the models' predictions on the testing data at specific points in the PR curve using the 0.25, 0.5, and 0.75 threshold values (lax, regular, and conservative). Table 5 shows that model 3 obtains the best overall results over the testing data, although model 1 detects more overtopping using the 0.25 threshold. Model 3 performs better at detecting positive and negative cases for higher thresholds. Both models 1 and 2 cannot detect any overtopping when using a 0.5 threshold in model 1 and 0.75 in model 2. As seen when using the training data, this fact indicates that the positive predictions of models 1 and 2 have low values, i.e., they predict values up to a point (less than 0.5 for model 1). In contrast, model 3 outputs values closer to 1 when it predicts an overtopping using the testing data. Table 6 summarises the precision, recall, and F\({}_{1}\) score for each class label (1: overtopping, 0: no overtopping) using 0.25, 0.5, and 0.75 thresholds for the model's predictions on the testing data for the models trained on the three datasets.