## 4 Discussion
In contrast, the number of parameters in a neural network model--where most parameters effectively measure interactions between variables--is typically much too large to maintain a proper balance between model sensitivity and overfitting. The obtained results confirm these intuitive observations. Most of the estimated linear models, as well as the non-linear models obtained for the building project dataset, share a common characteristic--they tend to overestimate the usable area of smaller houses while underestimating the feature in larger houses. This suggests difficulties in generalising at the domain's extremes, leading to a narrowingof the area onto which explanatory variable sets are projected. A probable cause is the small number of observations in the training sample within these regions, so increasing the sample size should significantly mitigate this issue. A preliminary test was conducted using the SMOTE method for regression tasks [52; 53], generating synthetic observations with large floor areas. However, no better results were obtained compared to the original training sample. Nonetheless, this remains a promising technique that could be utilised in future research. It appears that the key prerequisite for its successful application is the precise classification of studied objects and the identification of homogeneous, underrepresented classes within the training sample. In regression tasks, this is particularly challenging due to the continuous nature of the dependent variable, making it more difficult than in classification problems [54; 55]. In the part of the study that continued the work from [1], the results were better for the group of existing buildings than for the design dataset. In both cases, machine learning models, despite slight overfitting, demonstrated superior performance in predicting usable floor area compared to regression models. This appears to be primarily due to greater data consistency and higher quality in terms of modelling requirements. On the other hand, the relatively high error values may be due to an unfortunate selection of objects for the test dataset. Specifically, in a separate study on the project dataset, it was confirmed that when the test set properly reflects the structure of the entire population, model errors decrease several times. This provides further confirmation that the most crucial factor influencing the obtained results is data quality, and above all, the homogeneity of the training data. At the same time, this provides another argument for dividing the modelled group of buildings into smaller, more cohesive sets of homogeneous building types and conducting modelling within these subgroups. The clear advantage of machine learning models also suggests that ML models should be preferred for estimating usable floor area when high-quality data are available (especially data that cover the entire domain). Conversely, traditional regression models are more resilient to declines in data quality (e.g., missing or sparse data in certain areas of the domain). This is an important practical conclusion. LiDAR-derived data are available in many European Union countries [56], making national sources a viable basis for conducting similar analyses. The method described in this article could be applied in other countries having such data, although further research is needed to validate this hypothesis. Given that the models consider only the general characteristics of buildings, the proposed approach appears to have a significantly broader application.