## 6 Conclusions  
The main objectives of this study were to highlight the substantial benefits of combining domain expertise with decision tree construction and to offer domain experts a practical tool for creating visually guided decision trees. We have developed an interactive webtool, named GDT, for data handling; visualization (using SC and LDA); guided decision tree creation; and finally, comparison of the main parameters of the classification accuracy with the DecisionTreeClassifier within the scikit-learn library of Python. From GDT, the users can split the dataset into training and testing sets, create nodes, and split the training set according to [9]. The specific tasks that the tool performs to meet the needs of the method are as follows: (i) splitting the dataset in train and test set, (ii) allowing the user to select features, (iii) creating and managing the subsets belonging to different nodes, and (iv) visualizing, managing, and editing the decision tree in a proper way. We have published GDT and its source code (in Python, using Dash, Plotly, scikit-learn, pandas, and other packages) online ([[https://github.com/mohedano-munoz/decision_tree_builder](https://github.com/mohedano-munoz/decision_tree_builder)]([https://github.com/mohedano-munoz/decision_tree_builder](https://github.com/mohedano-munoz/decision_tree_builder)), accessed on 8 November 2024) so that analysts can use it and visualization experts can extend and adopt it to their needs. To demonstrate the utility of the tool, we presented a case study on motion recognition. In this case study, the manually constructed decision trees achieved slightly lower accuracy than those generated by sci-kit learn, but they were simpler and more interpretable. The manual construction process enabled domain experts to build more straightforward decision trees, gain valuable insights from the data, and apply their prior knowledge in \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Scikit-Learn Tree**} & \multicolumn{2}{c}{**Guided Decision Tree**} \\ \hline
**Feature** & **Nodes** & **Feature** & **Nodes** \\ \hline R\_Shoulder\_3 & 47 & R\_Hip\_1 & 20 \\ R\_Elbow\_2 & 42 & L\_Hand\_1 & 20 \\ Spinel\_1 & 40 & L\_Shoulder\_3 & 19 \\ L\_Hand\_3 & 40 & L\_Hand\_3 & 19 \\ L\_Elbow\_2 & 38 & L\_Collar\_2 & 16 \\ L\_Foot\_3 & 34 & R\_Shoulder\_3 & 16 \\ L\_Collar\_2 & 32 & R\_Knee\_1 & 14 \\ R\_Hip\_1 & 32 & Spinel\_1 & 14 \\ L\_Knee\_1 & 32 & L\_Knee\_1 & 13 \\ R\_Knee\_1 & 32 & L\_Elbow\_2 & 13 \\ R\_Ankle\_3 & 29 & L\_Collar\_3 & 13 \\ L\_Hand\_1 & 29 & R\_Elbow\_2 & 11 \\ L\_Collar\_3 & 28 & L\_Foot\_3 & 10 \\ L\_Shoulder\_3 & 26 & R\_Ankle\_3 & 10 \\ L\_Foot\_2 & 23 & L\_Foot\_2 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top #15 variable occurrences in the decision nodes. Figure 17: Zoomedin view of Figure 15a, focusing on the early stages, where it can be seen, for instance in the nodes highlighted with the red boxes, that the variable ‘R_Hip_1’ is a decisive node. the classifier construction. Our findings underscore the importance of a collaborative approach in machine learning, where human insight and computational efficiency work hand-in-hand to achieve better results. In this case, thanks to the GDT tool, domain experts confirmed that upper body features, particularly hip and shoulder rotation, along with hand movements, play a crucial role in defining motion and are more relevant within motion recognition. In future work, we plan to use GDT in different areas that require a selection of the most important variables in explainable models or that benefit from understanding the underlying processes. Fields such as health, social welfare, and meta-human utilization require decision trees that are more understandable and closer to the end user to validate their versatility and effectiveness. Additionally, there is potential for further development of both the tool and the method. For instance, implementing the C5.0 algorithm could provide an alternative for automatic tree construction.