## 3 Results and Discussion
### Validations and Analysis
Although the Decision Tree Regression (DTR) model did not always outperform others in raw accuracy metrics, it consistently exhibited excellent generalization and stability, as evidenced by low \(\Delta\)R\({}^{2}\) values across all properties. This stability likely stems from its hierarchical, rule-based structure, which effectively partitions the data and identifies dominant threshold effects. In contrast, Random Forest (RF), while effective during training, showed signs of overfitting on the test set. This is likely due to the model's tendency to memorize noise patterns when too many trees or weak learners are included without proper regularization. Finally, Linear and Ridge Regression models generally underperformed due to their limited capacity to model complex nonlinearities, although Ridge Regression showed slightly better robustness in cases of multicollinearity. These findings support the conclusion that NNR, SVR, and DTR each offer unique strengths in foam concrete property prediction and should be selected based on the specific performance target and data characteristics. Specifically, NNR reached the best performance in predicting compressive strength (R\({}^{2}\) = 0.9869, MAE = 1.6123 MPa) and flexural strength (R\({}^{2}\) = 0.9319, MAE = 0.37 MPa) [121; 122]. In contrast, SVM demonstrated superior performance in predicting thermal conductivity  
Figure 14: A flow diagram representing the K-fold validation process and final assessment of model performance. (R\({}^{2}\) = 0.9327, MAE = 0.0228 W/m\(\cdot\)K). DTR showed excellent generalization capabilities, with an average performance improvement of 32.7% on the test set. In compressive strength prediction, the MAE decreased significantly from 2.161 MPa to 1.019 MPa--a reduction of 52.8%. Error analysis revealed a correlation in predictive accuracy across different target properties [123]. The highest accuracy was found in thermal conductivity predictions (MAE = 0.0385 W/m\(\cdot\)K), followed by flexural strength (MAE = 0.4225 MPa), while compressive strength remained the most difficult task (MAE = 2.8926 MPa) [124]. The analysis of model stability reflected that DTR and NNR exhibited the most robust performance, with an average \(\Delta\)R\({}^{2}\) of 0.102, substantially outperforming LMR, which had an average \(\Delta\)R\({}^{2}\) of 0.284 [125; 126]. Figure 15: Integrated evaluation of machine learning algorithms. **(a)** Fc; **(b)** Fr; **(c)** TC. The performance of the proposed models is generally consistent with or slightly better than those reported in recent studies. For example, [PERSON] et al. [127] developed an optimized artificial neural network (ANN) to predict the compressive strength of concrete, achieving an R\({}^{2}\) of 0.963 using a dataset. In this study, the Neural Network Regression model achieved an R\({}^{2}\) of 0.987 with 170 samples, suggesting improved generalization through a more diverse dataset and 5-fold cross-validation. For thermal conductivity, [PERSON] et al. [128] applied ensemble machine learning models, including XGBoost and CatBoost, on a larger dataset of 504 cement-based foam concrete samples. Their best-performing models achieved R\({}^{2}\) values exceeding 0.98. Although our support vector regression model achieved a slightly lower R\({}^{2}\) of 0.933, it is still within an acceptable range considering the heterogeneity of the input data compiled from multiple literature sources. These comparisons indicate that the models developed in this study offer competitive accuracy while maintaining robustness across different performance indicators. As observed from Figure 16, feature importance scores were derived from the Random Forest models using Gini-based metrics. The top-ranked variables for strength prediction included W/C, cement content, and density, while thermal conductivity was most influenced by density and Cag/Fag ratio. Based on the error and stability analysis, a tiered model selection strategy was proposed to balance efficiency and predictive accuracy.