# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 6 Results
However, decision tree, random forest, KNN and SVR also depict good prediction capabilities. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine Learning** & **MSE** & **R\({}^{2}\)** \\
**Algorithm** & 0.056 & 0.9023 \\ \hline Linear Regression & 0.004 & 0.9934 \\ Decision Tree & 0.004 & 0.9935 \\ Random Forest & 0.004 & 0.9991 \\ Gradient Boosting & 0.000 & 0.9991 \\ KNN & 0.037 & 0.9344 \\ Support Vector Regression & 0.004 & 0.9934 \\ LightGBM & 0.001 & 0.9999 \\ CatBoost & 0.000 & 1.0000 \\ Linear Regression & 0.056 & 0.9023 \\ Decision Tree & 0.004 & 0.9934 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MSE and R\({}^{2}\) values of different models for fatigue life reduction. Figure 8 shows the performance prediction in terms of number of passes to fatigue damage for different models. Figure 8a shows the comparison of MSE for predicted and actual values in terms of number of passes to fatigue damage using the decision tree algorithm. Higher magnitude of MSE values correspond to an increased number of passes to fatigue damage in the range of millions. A total of seven folds have been developed. Fold 6 yields the minimum MSE of 299,531,256.21. The decision tree algorithm exhibits increased prediction performance with little scatter at higher number of passes to fatigue. In Figure 8b, comparison of predicted and actual values is shown using gradient boost. As observed, predicted values stayed on the perfect prediction line exhibiting no scatter. A total of eight folds have been developed. Fold 2 yields the least MSE of 12,288,384.14. Gradient boost outperforms other models with reduced scatter. In Figure 8c, predicted values are compared against the actual values using the K-neighbour algorithm. Increase in scatter from the actual values can be observed at a higher number of passes. A total of seven folds have been developed, with Fold 7 yielding the minimum MSE of 3,922,721,043.05. In Figure 8d, predicted values are compared against the actual values using linear regression. Scatter from linear regression is slightly reduced when compared to K-neighbour. Furthermore, MSE has been further reduced to 2,627,866,600.58 at Fold 3. The scatter increases as the number of passes are increased  
In Figure 8e, predicted values are compared against the actual values using random forest. A total of 10 folds have been developed, with Fold 7 exhibiting the least MSE of  
Figure 8: Number of passes to fatigue damage of different models: (**a**) decision tree; (**b**) gradient boosting; (**c**) KNN; (**d**) linear regression; (**e**) random forest; (**f**) SVR; (**g**) CatBoost; (**h**) LightGBM. 234,627,574.41. Scatter in predicted values is better than that of KNN and linear regression. Predicted value are at the perfect prediction line at a lower number of passes; however, increase in scatter can be observed at passes more than 1.2 \(\times\) 10\({}^{6}\). In Figure 8f, predicted values are compared against actual values using SVR. As observed, SVR shows the highest scatter and offset in predicted values when compared to actual values among other algorithms. Fold 4 exhibits the least MSE of 48,122,037,546.27. Performance of LightGBM based on predicted and actual values is shown in Figure 8g. As observed, LightGBM shows overall accurate prediction capacity with slight scatter in prediction along the intermediate and higher number of passes due to non-linear behaviour of asphalt mixture related to fatigue damage. LightGBM yields MSE of 1.428 \(\times\) 10\({}^{8}\) and R\({}^{2}\) of 0.999. Predicted and actual values for CatBoost regression are shown in Figure 8h. As observed, predicted values stay on the perfect prediction line exhibiting highest prediction performance among other models. Least MSE of 3.755 \(\times\) 10\({}^{6}\) is observed with R\({}^{2}\) of 1.000.