# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results
### Model Training and Optimization
Table 4 presents the optimal hyperparameters for the two XGBoost models' instantiations (Baseline Model and Open Data Model), outlining their best-found values and detailing the respective search ranges for these parameters for each model. Both models demonstrate a balanced strategy in subsampling ratios and feature selection (colsample_bytree), carefully managing the trade-off between model complexity and overfitting. The Open Data Model's reduced gamma setting allows for more extensive tree splits, thus enabling a finer granularity in capturing data patterns. With increased L2 (reg_lambda) and moderated L1 (reg_alpha), its regulatory strategy emphasizes feature efficacy while managing model complexity. A crucial aspect of the Open Data Model is the heightened min_child_weight, strategically set to prevent overfitting. Overfitting occurs when a model learns too much from the training data, including the noise and fluctuations, which are not generalizable. This adjustment is vital in real estate modeling, characterized by market variability across locations, property types, and economic conditions, and an overly tailored (overfitted) model fitted to a market segment, such as a particular location or property type, risks underperforming in other segments. The deliberate enhancement of min_child_weight in the Open Data \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Description** & **Type** & **Baseline Model** & **Open Data Model** \\ \hline \multirow{4}{*}{n\_estimators} & The number of gradient boosted trees. & \multirow{4}{*}{Integer} & \multirow{4}{*}{1961} & \multirow{4}{*}{2031} \\  & Equivalent to the number of boosting rounds. & & & \\  & Too many trees can lead to overfitting by & & (1800, 2100) & (2000, 2050) \\  & capturing more noise. & & & \\  & Maximum tree depth for base learners. & \multirow{4}{*}{Integer} & \multirow{4}{*}{8} & \multirow{4}{*}{11} \\  & Controls how deep each tree is allowed to grow during any boosting round. Deeper & & (6, 8) & (10, 11) \\  & trees can capture more noise. & & & \\  & Step-size shrinkage is used to prevent & \multirow{4}{*}{Float} & \multirow{4}{*}{0.007} & \multirow{4}{*}{0.013} \\ learning\_rate & overfitting. It scales the contribution of each & & (0.007–0.010) & (0.012, 0.013) \\  & tree by a factor between 0 and 1. & & \\  & The proportion of data samples utilized to & \multirow{4}{*}{Float} & \multirow{4}{*}{0.74} & \multirow{4}{*}{0.72} \\ subsample & & less than 1 make the algorithm more & \\  & conservative and prevent overfitting. & & \\  & The fraction of features to be used for each & \multirow{4}{*}{Float} & \multirow{4}{*}{0.57} & \multirow{4}{*}{0.60} \\ colsample\_bytree & tree. A value of 0.6 means that 60\% of features & & \\  & are used to train each tree. & & \\  & Required minimal reduction in loss to further & \multirow{4}{*}{Float} & \multirow{4}{*}{0.27} & \multirow{4}{*}{0.10} \\ gamma & split a leaf node in the tree. A larger gamma & & \\  & simplifies the model, preventing overfitting. & & \\  & L2 regularization term (Lasso) on weights. It & \multirow{4}{*}{Float} & \multirow{4}{*}{1.95} \\ reg\_lambda & is used to avoid overfitting by penalizing & & \\  & L1 regularization term (Ridge) on weights. It & & \\ reg\_alpha & encourages sparsity in the final model & \multirow{4}{*}{Float} & \multirow{4}{*}{1.84} \\  & representation, allowing some features to be & \\  & entirely ignored. & & \\  & Minimum sum of instance weight (hessian) & & \\ min\_child\_weight & required in a child node. Higher values help & \multirow{4}{*}{Integer} & \multirow{4}{*}{3} & \multirow{4}{*}{11} \\  & prevent overfitting by avoiding learning from & & \\  & overly specific data patterns. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal hyperparameters of the XGBoost model (best value and search range). Model reflects a strategic choice to maintain the model's versatility, as the model ensures wide-ranging applicability by avoiding overemphasizing specific market segments.