ScalingNone

ScalingNone refers to a setting in data processing or machine learning where no adjustment is made to the raw data or features. This means the original values are preserved as they are, without normalization or transformation. In practice, selecting ScalingNone can be useful when the data is already on a comparable scale or when the natural distribution of the data is important for the analysis. It ensures that the inherent relationships and relative differences between data points remain intact, allowing models or procedures to work directly with the original values without additional modifications.