## 5 Data Analysis and Method Implementation
### Results and Analysis
Outcome after altering the number of samples per split are illustrated in Figure 22. The mean accuracy presented in Table 11 illustrates that an intermediate number of samples per split give decent result, e.g., 65 to 67% for 4 to 6 sample size per split. As the number of samples per split increases, there is a decline in the accuracy. The boxplots illustrate slight skewness in the data distribution. There are few outliers for a high number of samples per split. Table 12 presents the result after tuning the number of trees in an ensemble. The scores shown on the right side for the mean accuracy of the model depicts that with a higher number of trees in an ensemble, the model performs better with the unseen examples. The boxplots in Figure 23 have an almost symmetric result and have overlapping means and median. There are very few outliers available in the sample data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Mean Accuracy (\%)** \\ \hline No. of floor & 68 \\ Total floor area & 70 \\ Column area & 69 \\ Concrete wall area (NS) & 68 \\ Concrete wall area (EW) & 68 \\ Masonry wall area (NS) & 69 \\ Masonry wall area (EW) & 68 \\ Captive Columns & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Nepal: Predictive accuracy score against each input feature. Figure 21: Nepal: Boxplot to represent the mean accuracy distribution achieved by hyper-tuning the input features. Figure 23: Nepal: Boxplot represents the mean accuracy distribution achieved by tuning the number of trees for every ensemble. \begin{table}
\begin{tabular}{c c} \hline \hline
**No. of Sample per Split** & **Mean Accuracy (\%)** \\ \hline
2 & 65 \\
3 & 64 \\
4 & 67 \\
5 & 65 \\
6 & 65 \\
7 & 64 \\
8 & 64 \\
9 & 63 \\
10 & 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Nepal: Predictive accuracy score against number of samples per split. Figure 22: Nepal: Boxplot to illustrate the mean accuracy distribution by hyper-tuning the minimum number of samples per split. Pohang has got the minimum number of examples in its dataset, reflecting in the outputs. The outcomes after tuning the hyperparameters are not symmetric for most cases. Before configuring the hyperparameters or else tuning the number of trees, the number of samples per split is below the scale. Before accommodating the hyperparameters, Pohang showed an accuracy of 67%. Only after tuning the features, the accuracy score is high, as it is clear from Table 13. Every input feature has almost achieved similar accuracy in between 70% to 72%. The boxplot results shown in Figure 24 show skewness, which is apparent as almost all the means and median are non-overlapping except for the Masonry wall area (EW); few outliers are also visible in the sample data. Table 14 shows the mean accuracy results after tuning the number of samples per split. With 2-9 samples, the model performed moderately while the higher value, like 10 number of samples per split, degrades the accuracy. The boxplots shown in Figure 25 have skewness in the distribution, and there are few outliers in the sample input. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Mean Accuracy (\%)** \\ \hline No. of floor & 72 \\ Total floor area & 70 \\ Column area & 71 \\ Concrete wall area (NS) & 70 \\ Concrete wall area (EW) & 72 \\ Masonry wall area (NS) & 71 \\ Masonry wall area (EW) & 71 \\ Captive Columns & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Pohang: Predictive accuracy score against each input feature. Figure 24: Pohang: Boxplot to represent the mean accuracy distribution achieved by hyper-tuning the input features. Results in Table 15 are for mean accuracy achieved after hyper-tuning the number of trees in an ensemble. The result shows that a moderately higher number of trees make a compelling ensemble and efficient accuracy. The boxplots illustrated in Figure 26 show essentially similar patterns except for the ensemble with ten trees. There are a few outliers present as well with the smallest ensemble.