## 5 Data Analysis and Method Implementation
### Results and Analysis

Table 6: Ecuador: Predictive accuracy score against number of trees for each ensemble. Figure 16: Ecuador: Boxplot to illustrate the mean accuracy distribution by hyper-tuning the minimum number of samples per split. Figure 17: Ecuador: Boxplot to represent the mean accuracy distribution achieved by tuning number of trees for every ensemble. The boxplot result is comparatively asymmetric for tuning the sample per split, and few outliers are present in the input data. Accuracy data on Table 8 present similar results like Ecuador's performance. A fewer or moderate number of samples per split have better predictive accuracy than the higher samples per split. The boxplots on Figure 19 side are not symmetric, and they are somewhat skewed. The outcome of tuning the feature parameters and the number of samples per split does not significantly change the model's average score. Only the number of trees has shown growth in predictive accuracy in Table 9 as compared before and after applying hyperparameter tuning. In addition, 100 is the default hyperparameter value for the number of trees, but, based on the sample size, it may vary from 10 to 5000. The individual box plot and the mean accuracy scores clearly show that the score is lowest when the tree numbers are less, and the score goes high when the number of trees is 30. Further increase in tree number might not be useful in scoring high accuracy as the sample dataset is small. The median of the box plot and the mean accuracy score is not necessarily overlapping except for an ensemble with 40 trees. The predictive accuracy list shows that 20 to 30 trees for creating the ensemble moderately fit the model samples. A similar result is showcased in the boxplots in Figure 20. With 30 trees, the model has shown higher accuracy, and it is skewed left (negatively skewed) according to Figure 20 and Table 9. The whisker line for 20 number of trees is visibly drawn towards the bottom end, which indicates the practical value of the data allocation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Mean Accuracy (\%)** \\ \hline No. of floor & 61 \\ Total floor area & 60 \\ Column area & 62 \\ Concrete wall area (NS) & 64 \\ Concrete wall area (EW) & 63 \\ Masonry wall area (NS) & 63 \\ Masonry wall area (EW) & 63 \\ Captive Columns & 66 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Haiti: Predictive accuracy score against each input feature. Figure 18: Haiti: Boxplot to represent the mean accuracy distribution achieved by hyper-tuning the input features. Figure 19: Haitit: Boxplot to illustrate the mean accuracy distribution by hyper-tuning the minimum number of samples per split. \begin{table}
\begin{tabular}{c c} \hline \hline
**No. of Sample per Split** & **Mean Accuracy (\%)** \\ \hline
2 & 64 \\
3 & 65 \\
4 & 64 \\
5 & 66 \\
6 & 62 \\
7 & 63 \\
8 & 62 \\
9 & 61 \\
10 & 62 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Haitit: Predictive accuracy score against number of samples per split. Figure 20: Haitit: Boxplot represents the mean accuracy distribution achieved by tuning the number of trees for every ensemble. Figures 21-23 are the results for accuracy scores generated by models for Nepal after tuning the hyperparameters. Nepal achieved no compelling improvement in accuracy score, compared to what it achieved before optimizing the hyperparameters. One possible reason behind the result could be the high variance of the test harness. In Table 10, the results after hyper-tuning different feature parameters are presented. The mean accuracy shows that, overall, all the features attain a similar accuracy; total floor area and Captive column are slightly on the higher side. The boxplots on Figure 21 illustrate that most data distribution is between the 25 th and 50 th percentile. The boxplots are skewed in nature.