👉 Unlevelness is a phenomenon in data analysis where certain variables or features are not evenly distributed across different groups, resulting in differences between groups. This can be caused by various factors such as missing values, outliers, or other anomalies in the dataset. In machine learning and statistics, unlevelness is often used to assess the quality of the data and identify potential issues with model performance. It is important for researchers and analysts to understand and manage this issue to ensure that their models are accurate and useful.
