# Spatial Image-Based Walkability Evaluation Using Regression Model
## 3 Experimental Results
### Retraining the Regression Models  
Using the 66 features listed in Table 7, a study was conducted to enhance the accuracy of XGBoost, which exhibited the best performance among the five regression models. The model was trained incrementally, from 30 to 66 features, using an exhaustive search algorithm, and its performance was showed through graphs. XGBoost exhibited the highest accuracy (77.24%) when trained on 38 features, an increase of 0.64% compared to when it was trained on 150 features (76.60%). The performance of the model by increasing the number of features in increments of 2 (30, 32, 34,..., 62, 64, 66) is shown in Figure 6, and the 38 features resulting in the highest XGBoost accuracy are listed in Table 8. It is worth noting that three features (\"rock\", \"stroke\", and \"awning\") were excluded from the XGBoost feature importance listed in Table 6, and the features \"person\", \"base\", \"steps\", \"bridge\", \"refrigerator\", \"palm\", \"grandstand\", \"apparel\", \"flag\", and \"sand\" were newly extracted.  
Other models were retrained using 38 features of importance optimized for XGBoost. In Figure 7, the accuracies of the models trained on 150 features in Section 3.1 was compared with those of the models trained on the 38 features of importance. The accuracy of multiple linear regression decreased by 0.11% to 71.04% and that of ridge regression decreased by 0.17% to 71.02%. However, the accuracies of the LASSO regression and SVR trained on 150 or 38 features remained unchanged. Moreover, the accuracy of XGBoost increased by 0.64%, showing the greatest performance improvement. It was determined that 38 features are the optimal number for XGBoost, while the number of features suitable for other regression models varies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Multiple** & **Ridge** & **LASSO** & **SVR** & ** XGBoost** \\ \hline
15 & truck & person & pool table & base & rock \\
16 & dirt track & ashcan & counter & plant & truck \\
17 & trade name & traffic light & chest of drawers & box & railing \\
18 & van & railing & signboard & van & bag \\
19 & railing & dirt track & column & pot & ashcan \\
20 & field & plant & box & house & traffic light \\
21 & person & bag & case & traffic light & bus \\
22 & traffic light & pot & stairway & bicycle & field \\
23 & house & field & pillow & path & minibike \\
24 & pot & awning & flower & water & box \\
25 & streetlight & minibike & palm & bridge & van \\
26 & base & signboard & stove & bench & sculpture \\
27 & minibike & bridge & counter top & stairway & trade name \\
28 & mountain & base & bench & windowpane & pot \\
29 & ashcan & water & hill & apparel & awning \\
30 & rock & ship & book & minibike & mountain \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline road & sidewalk & tree & sky & signboard & building \\ car & plant & earth & pole & fence & person \\ traffic light & pot & minibike & wall & streetlight & grass \\ truck & mountain & field & base & railing & box \\ van & bus & trade name & ashcan & dirt track & bag \\ stairway & path & awning & water & bridge & bench \\ sculpture & rock & flag & runway & stairs & grandstand \\ refrigerator & fireplace & skyscraper & sand & sink & pool table \\ ceiling & counter & chest of drawers & column & case & bicycle \\ house & pillow & flower & palm & stove & counter top \\ windowpane & hill & apparel & ship & book & door \\ \hline \hline \end{tabular}
\end{table}
Table 7: Feature importance of each model after removing duplicates.