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Year 2026, Volume: 36 Issue: 1, 60 - 70, 30.03.2026 Machine learning , ensemble learning , multi-output regression , textile , apparel This study has been supported by the project numbered 119C078 carried out within the scope of the TUBITAK 2244 Industry Doctorate Program. The authors would like to thank TUBITAK and TYH Textile for their assistance and support. 1. Lee C-Y, Lin J. 2018.

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4.2. Experimental Results The balanced dataset was divided as a training set and test set through the 10-fold cross-validation technique with the aim of applying the proposed LMT forest method to it. According to this approach, the results of each fold are separately given in Table 4. As can be seen, our proposed model outperformed the well-known random forest method [81] in terms of classification accuracy.