# The Role of Data-Driven Methodologies in Weather Index Insurance
## 5 Results and Discussion  
The results are presented next. The first part refers to model building and then the performance and selection of the models are carried out, including insights into the costs for both parties. In model building, there are multiple hyperparameters, such as the loss function, the optimizer used, the number of layers and nodes, and the activation function. Each of these hyperparameters can be chosen from a large number of options, but there is no \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **Observed** \\ \hline
**User** & **Prediction** & **Yes** & **No** \\ \hline Insured part & Yes & \(E_{p}(q)\) & \(E_{p}(q)-E_{a}\) \\  & No & \(E_{p}(q)+E_{n}\) & \(E_{p}(q)\) \\ Insurer & Yes & \(E_{c}\) & \(E_{c}+E_{r}\) \\  & No & \(E_{l}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cost of events for the users. clear indication of the number of hidden layers or nodes that should be used for a specific problem. According to [PERSON] [28], it is currently considered that the number of units per layer can be high, and over-fitting can be controlled by making use of the various forms of regularization. Accordingly, the space of configurations for the neural network must be limited, since it is practically infinite. For this reason, a range of values was selected for each of the hyperparameters of the model, which are summarized in Table 6.  
A space of 27,000 configurations is explored, using 15 combinations of the data series. The selected combinations were chosen as follows:  
1. Possible combinations from two to four precipitation data series of indices were tested. Specifically, the three precipitation data series and the surface flow data series. This results in 11 possible combinations. 2. Subsequently, each of the soil moisture levels were added sequentially. This corresponds to 4 additional combinations of the model inputs. Referring to the pre-processing techniques, there was a severe class imbalance, where the ratio of the minority class to the majority class was approximately 1:16. For this specific problem, oversampling, SMOTE, and a mixture between undersampling and oversampling were selected as pre-processing techniques, specifically to improve the sensitivity of the implemented models. Regarding the selection of the best models and in line with the methodology used by [PERSON] [24], we used several performance metrics: F1 score and the area under the precision-sensitivity (PS) or the AUC score. Additionally, we used a utility measure to derive the optimal cut-off point which also serves to derive the costs related to each model. When using two metrics, they will not necessarily coincide when selecting the best model, so the models selected with an F1 score are analysed first, followed by those selected with the AUC. In this study, we used a logistic regression model as a baseline to compare the performance of the best models. Therefore, the performance of the models shows that when using:  
* F1 score as a selection criterion: the best model is obtained using seven datasets, a combination of undersampling and oversampling as a pre-processing technique, one hidden layer, and ReLU as an activation function. The hidden layer's dropout rate is 0.4. This architecture corresponds to a simple neural network, with an intermediate level of regularization.