# The Role of Data-Driven Methodologies in Weather Index Insurance
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Values** \\ \hline \hline \multirow{7}{*}{\begin{tabular}{c} Neural \\ Network \\ \end{tabular} } & \begin{tabular}{c} Combinations of input datasets \\ \end{tabular} & 15 combinations \\ \cline{2-3}  & \begin{tabular}{c} Unweighted \\ Oversampling \\ SMOTE \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Combination Undersampling and Oversampling \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Loss function \\ \end{tabular} & \begin{tabular}{c} Binary Cross Entropy \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Optimizer \\ \end{tabular} & \begin{tabular}{c} ADAM * \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Number of layers \\ and nodes \\ \end{tabular} & \begin{tabular}{c} Layers: [1:9] \\ Nodes: 100 \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{c} Dropout and rate \\ layers \\ \end{tabular} & \begin{tabular}{c} Layers dp: [1:9] (same as \# Layers) \\ rate dp: [0.0, 0.2, 0.4, 0.6, 0.8] \\ \end{tabular} \\ \cline{2-3}  & \
\begin{tabular}{c} Activation functions \\ \end{tabular} &
\begin{tabular}{c} ReLU \\ Tanh \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Number of configurations} & 27,000 \\ \hline \hline \end{tabular}  
* ADAM is “an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments” [40].  
\end{table} 
Table 6: Explored configuration space. * The area under the precision-sensitivity curve (AUC): the best model uses seven input datasets, a mixture between undersampling and oversampling as a pre-processing technique, four hidden layers, and the ReLU activation function. The dropout rate for each layer is 0.0, 0.6, 0.6, and 0.6, respectively. On the other hand, when comparing the best model architectures with those obtained by [PERSON] [24], it is evident that the model complexity here studied is significantly lower than the one obtained in that study. Even though deeper structures were explored, results showed poorer performance metrics due to possible overfitting during training. This low performance may happen for several reasons. One possible failure in the reporting processes for catastrophic events in low-income economies, such as the one studied here for flood events, is a possible lack of coherence between the actual flood date and the reported one. As mentioned by [PERSON] [27], the performance of a model can be affected by using poor data as is the case here. Figure 7 shows the performance of the best 27 configurations in the precision-recall space, which also includes level curves corresponding to the F1 score. The precision-sensitivity curve allows each model to be evaluated for the whole range of cut-off probabilities. The perfect model resides in the upper right corner, with a precision and sensitivity of 1. Based on this result, the performance of the best models is far from the optimal model and closer to the baseline model. The best performance of the neural network models is associated with an F1 score of 0.2989, while the logistic regression model achieves a maximum F1 score of 0.2739. Regarding the AUC metric, the machine learning model achieves a score of 0.214, while the logistic regression model achieves an AUC score of 0.205. Table 7.  
Figure 8 compares the F1 score evaluated in the test data for an increasing number of input datasets. Neural network results were averaged for three iterations, due to the stochastic component related to model training. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **F1 Score** & **AUC** \\ \hline Neural Network & 0.290 & 0.214 \\ Logit & 0.274 & 0.205 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model comparison, F1 score selection. Figure 7: Top 0.1% configurations according to F1 score. Regarding the indices that better explain the losses, the results show that the machine learning algorithm achieves a better result, but its performance is not superior in all cases.