Data quality is critical to effective deployment of ML models . However, experimental measurements are prone to errors, which raises concerns regarding data reliability. To address this concern, outliers (molecules whose solubility cannot be reliably predicted) can typically be identified using descriptor dissimilarity or clustering methods. In recent work it was proposed that outliers of solubility datasets could be detected based on the prediction error distribution . The data were divided into k training and testing datasets. As a result, the samples in each testing set are predicted k times by the models trained on distinct data. Samples that frequently fall beyond a predetermined error range are considered outliers. Nevertheless, this approach may be inefficient for smaller datasets, high-dimensional feature spaces, or complex ML models.