###### Abstract  
Decision trees are a widely used machine learning technique due to their ease of interpretation and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher's criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes' information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition. multivariate visualization; decision trees; visual data mining; linear discriminant analysis; motion recognition 20  
A way to include domain experts in the model-building process is through interactive visualization. Interactive visualization has been proven an efficient way of improving the knowledge obtained by the domain experts in data analysis processes. User interaction can improve and modify the models obtained, increase researchers' confidence in the model, and give them a better understanding of the processes underlying the data. Machine learning algorithms can be categorized as supervised learning and unsupervised learning, according their target and data requirements. While clustering is an unsupervised technique used to group samples by a similarity criterion, classification tasks fall within supervised learning techniques, and their aim is to predict the categorical value of unlabeled elements from a group of labeled elements [3]. A classifier is then a machine learning model that, given a sample with different variables, predicts a label from the samples learnt in the training process from a specific pre-labeled, training dataset. One of the most well-known methods employed in classification tasks is decision trees. Classification trees successively divide the dataset according to the fulfillment of different criteria until they define the conditions that characterize the different classes present in the training dataset [4]. Specifically, decision trees define the division criteria according to automatic algorithms, which try to maximize the gaining of information on every subset construction or the value of the Gini impurity measure. Nevertheless, this kind of algorithm does not allow researchers to include their domain knowledge and makes it difficult for them to understand the model's inner workings [5; 6]. Moreover, for complex datasets or some algorithms' options, the tree might be too big or complex to grasp at a glance how the classification is made [7]. In some contexts, this may limit real-world applications or completely hinder them [8].