Several decision trees comprise the ensemble classification model, Random Forest (RF) . It is created via random variable selection and bagging. The principle behind constructing trees in Random Forest is similar to that of decision trees, relying on recursive partitioning. In recursive partitioning, the distribution of observations in the training sample is crucial for determining the precise location of the cut-point and selecting the splitting variable. Since small changes in the training data can influence the choice of the initial split variable or cut-point, decision trees tend to be unstable classification models. Consequently, the structure of a decision tree will change as well. Random Forest addresses this instability by combining multiple trees. While a single decision tree may be affected by fluctuations, averaging the predictions of numerous trees leads to more reliable results. However, if the trees in the forest are too similar, the model's classification accuracy can decline. Therefore, diversifying the tree population is essential for enhancing the model's categorization performance. Random Forest achieves this diversification through the randomization of both the input variable sets and the training data sets. RF is commonly used as a baseline model for performance comparison in studies that involve multi-label datasets . Details regarding the model's hyperparameters can be found in supplementary file 2.