Following dimensionality reduction, the unsupervised K-Means clustering method was directly performed on the two-dimensional data obtained from the t-SNE transformation to categorize the data points into two distinct clusters. For the experimental dataset, cluster 1 contains 420 data points and is used as training data, , testing them under out-of-distribution (OOD) conditions. This is done by randomly selecting 80% of the data from both training and testing clusters. The chosen algorithms, as previously mentioned, are applied to train and test using this subset, having undergone hyperoptimization to maximize the R 2 value. This sampling and evaluation process is repeated 10 times, with the resulting metrics being averaged and presented in Table .