## 4 Evaluation and Results
### Machine Learning Applied to Data Analysis
#### 4.2.1 Decision Tree Analysis: Dominant Features by STP Type  
Decision tree analysis is used for classification tasks. It could suggest the most dominant features for each type of STP, i.e., the critical factors describing each type of STP based on the experimental data. This supervised learning algorithm incorporates the STPs and their core features, using STP type as a decision factor, where it only considers SPs and TPs as part of the data training and includes HPs in the tree classification. The core features considered in the decision tree analysis are the 12 presenting substantial differences by types of STP (Table 3). However, Fe15. Investment R&D and Fe19. Innovative profile 2 should be rejected, because over 16% of the data was missing, which could detract from the decision tree (force the aggrupation per missing data). Therefore, 10 STP core features have been considered (Fe02, Fe03, Fe04, Fe05, Fe06, Fe09, Fe10, Fe12, Fe13, and Fe18). Decision trees were grown for different subsets of the features, with different hyper-parameters related to tree depth and pruning. Figure 6 presents two two-level decision trees, as more levels would not bring any further relevant information: Tree 1, for the 10 core features; and Tree 2, for the 9 core features excluding size. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Core Feature** & \
\begin{tabular}{c} **Substantial Difference** \\ **between SP and TP** \\ \end{tabular} & **Core Feature** &
\begin{tabular}{c} **Substantial Difference** \\ **between SP and TP** \\ \end{tabular} \\ \hline Fe02. STP age & SP \(<\) TP & Fe12. Productivity & SP \(<\) TP \\ \hline Fe03. STP size & SP \(<\) TP and SP \(<\) HP & Fe13. Incubation ratio & TP \(<\) SP \\ \hline Fe04. Companies & SP \(<\) TP & Fe14. Employment R\&D & NO \\ \hline Fe05. Turnover & SP \(<\) TP & Fe15. Investment R\&D & SP \(<\) TP \\ \hline Fe06. Employment & SP \(<\) TP and SP \(<\) HP & Fe16. Filed patents & NO \\ \hline Fe07. International companies & NO & Fe17. Granted patents & NO \\ \hline Fe08. Incubated companies & NO & Fe18. Innovative profile 1 & TP \(<\) SP \\ \hline Fe09. Average company size 1 & SP \(<\) TP & Fe19. Innovative profile 2 & TP \(<\) HP \\ \hline Fe10. Average company size 2 & SP \(<\) TP and SP \(<\) HP & Fe20. Patents ratio 1 & NO \\ \hline Fe11. Internationalisation & NO & Fe21. Patents ratio 2 & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Permuted Brunner Munzel test results for 20 core features per STP type. Figure 6a,b presents an acceptable accuracy (0.86) that confirms a clear differentiation between training groups (TPs and SPs). While the trees only include TPs and SPs, in the detailed description, the given HPs are also classified according to their proximity to SPs or TPs. STP size is the obtained root node from Figure 6a and, therefore, is a very determinant feature in STP classification, with TP size being significantly higher than SP size (Fe03). However, the size in terms of square meters is usually a stable metric in the short term. Therefore, the decision tree analysis in Figure 6b is developed again excluding the size factor and to explore other determinant core features. Even when only the core features presenting substantial differences were considered in the decision tree analysis, the decision tree considering all the features has been also developed, obtaining the same results, which is very consistent because, as presented in Section 4.1, the rest of the core feature have no significant differences based on STP type. Considering the decision tree analysis and in line with the previous descriptive analysis, the following STP type profiles can be described:  
* The Spanish SP presents a surface area (Fe03. STP Size) under 200,000 m\({}^{2}\), Fe13. Incubation ratio > 0.14, and Fe18. Innovative profile 1 > 0.46 as the most significant feature. * The Spanish TP presents a surface area over 200,000 m\({}^{2}\), innovative profile 1 less than 0.46, and average company size 1 > 10.40.