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Current OOD studies primarily focus on extrapolatory (outside) OOD, neglecting potential cases of interpolatory (inside) OOD. In this study, we introduce a novel perspective on OOD by suggesting that it can be divided into inside and outside cases. We examine the inside\u2013outside OOD profiles of datasets and their impact on ML model performance, using normalized root mean squared error (RMSE) and\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    score as the performance metrics on synthetically generated datasets with both inside and outside OOD. Our analysis demonstrates that different inside\u2013outside OOD profiles lead to unique effects on ML model performance, with outside OOD generally causing greater performance degradation, on average. 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