## 4 Results
### Robustness Check
#### 4.3.3 Alternative Machine Learning Models and Ensemble Learning  
To validate the robustness of our modeling approach, we developed and assessed a range of alternative machine learning models, including Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM) with a linear kernel, and Elastic Net regression, alongside our primary Random Forest (RF) model. All models were tuned via 10-fold cross-validation, and their predictive performance was compared using RMSE, MAE, and R\({}^{2}\).  
Among the individual learners, Elastic Net achieved the best average performance (MAE = 0.224, RMSE = 0.297, R\({}^{2}\) = 0.907), followed closely by SVM and XGBoost. The Random Forest model, while slightly behind in overall prediction accuracy (MAE = 0.354, RMSE = 0.455, R\({}^{2}\) = 0.765), consistently demonstrated robust performance and stability across resamples.  
We further combined all models into an ensemble using a greedy error-minimizing strategy. The resulting ensemble achieved the best overall MAE (0.214), assigning dominant weight to Elastic Net (75.5%) and XGBoost (22.0%), while RF contributed only marginally (2.5%). This reflects the ensemble's bias toward models with minimal error variance in cross-validation.  
However, while ensemble blending improved raw accuracy slightly, its complexity and lack of interpretability limit its practical value in policy settings. In contrast, the Random Forest model provides direct access to variable importance metrics and compatibility with SHAP explanations, making it especially useful for identifying key drivers of cloud adoption.  
Crucially, across all models, ICT specialists emerged as one of the top three predictors, reaffirming the central role of digital workforce capacity (Table 6). Both XGBoost and RF ranked ICT specialists and broadband access as their top two predictors, while Elastic Net identified year and ICT specialists as the most influential variables.  
These findings underscore that while alternative models may offer slight gains in predictive accuracy, Random Forest remains the most appropriate tool for this analysis due to its explanatory power, transparency, and alignment with policy needs.  
To ensure transparency and reproducibility, we report the training time required for each model under standardized settings in Table 7. All models were trained on a MacBook Air 13\" (Apple M3 chip) using RStudio Version 2024.12.1 and R 4.3.1, with 10-fold cross-validation for internal tuning. The computations were performed using the care  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Top 1 Predictor** & **Top 2** & **Top 3** \\ \hline
**Random Forest** & ICT Specialists & Broadband Access & GDP per Capita \\
**XGBoost** & ICT Specialists & Broadband Access & Year \\
**Elastic Net** & Year & ICT Specialists & geo\_FI (Finland) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ranking of top predictors by model.  
Although the ensemble required the longest runtime, all models completed training in under 15 s, illustrating the feasibility of our approach even on lightweight consumer hardware. These timing benchmarks confirm that the proposed methods are not only statistically sound but also computationally efficient.