## 3 Results and Discussion
Therefore, for each dependent variable, the class material is indicated as the best predictor, but the grouping is different, as follows:  
For acoustic comfort (Figure 4), the decision tree split the class material into Node 1, which included metals, Glass-based materials, Plastic/polymers-based materials and Textile Materials, and Node 2, which included wood-based materials and elastic polymers. For the absorption acoustic class (Figure 5), the decision tree splits the class material into Node 1, with metals, glass-based materials, plastic/polymers-based materials and textile materials, and Node 2, with wood-based materials and elastic polymers; moreover, for Node 1, the second predictor is given by frequencies with a cutoff \(<\)/\(>\) 1700 Hz. For the acoustic class performance (Figure 6), the decision tree split the class material into Node 1, which included metals; Node 2, which included wood-based materials and elastic polymers; and Node 3, which included plastic/polymer-based materials and textile materials. The tree depicts the distribution of absorption acoustic classes (A, B, C, D and other classes) among different class materials. Node 0 shows the overall distribution, with class materials divided based on their type into Node 1 and Node 2. Node 1 includes metals, glass-based materials, plastic/polymer-based materials and textile materials, showing a varied distribution across absorption acoustic classes. Node 2 includes wood-based materials and elastic polymers, primarily categorized as 'Other class'. Further branching from Node 1 is based on frequency (Hz), with Node 3 representing frequencies \(\leq\)1700 and Node 4 representing frequencies \(>\)1700. The adjusted \(p\)-value is 0.000, with a Chi-square value of 42.561 for the main division and 25.921 for the frequency division, indicating significant relationships between the class material, frequency and absorption acoustic class. The tree illustrates the distribution of class materials across different absorption performance categories: very high-performance absorbent materials, absorbent materials and non-absorbent materials. Node 0 displays the overall distribution, with 17.0% classified  
Figure 6: Decision tree for absorption class performance. as very high-performance absorbent materials, 39.3% as absorbent materials and 43.7% as non-absorbent materials. The class materials are further divided into Node 1, Node 2 and Node 3 based on their types. Node 1 includes metals and glass-based materials, showing 10.3% as very high-performance absorbent, 51.9% as absorbent and 37.8% as non-absorbent materials. Node 2 includes wood-based materials and elastic polymers, with the majority classified as absorbent or non-absorbent materials. Node 3 includes plastic/polymer-based materials and textile materials, showing a distribution similar to Node 1 but with a higher percentage of non-absorbent materials. The adjusted \(p\)-value is 0.000, with a Chi-square value of 36.402 and four degrees of freedom, indicating a significant relationship between class material and absorption class performance. Specific global results:  
Acoustic comfort is significantly influenced by the type of material. Materials from class numbers 3 and 6, based on wood and elastomeric polymers, contribute to significantly lower improvements in acoustic comfort compared to materials from classes 1, 2, 4 and 5: metals, glass-based materials, plastics/polymers and textiles, which are more effective in this regard. This suggests that the physical and acoustic properties of materials significantly influence acoustic comfort. The class of acoustic absorption differs depending on the material type and frequency. Metals, glass-based materials, plastics/polymers and textiles (especially at frequencies above 1700 Hz) are more effective in sound absorption, indicating that these materials perform well at high frequencies due to their density and molecular structure.