nonnormalled

Nonnormalled is an adjective used to describe data, features, or objects that have not undergone normalization or standardization. In data science and analytics, normalization refers to transforming values to a common scale or distribution to improve comparability and model performance. Nonnormalled data retain original units, scales, and distributions, which can affect techniques that assume comparable ranges, such as distance-based algorithms or regularized models.

Etymology and usage: The term is informal and primarily found in technical notes or informal discussions within data-processing workflows. It combines the root normal with the past participle suffix to indicate a state of not having been normalized.

Applications: In preprocessing workflows, labeling a variable as nonnormalled signals that it should be considered for scaling, such as min-max normalization or z-score standardization, before fitting a model. Nonnormalled features may require transformation to avoid issues like skewness, large feature disparities, or biased model coefficients.

Limitations and implications: Treating nonnormalled data without addressing scaling can lead to biased coefficients, inflated error, or poor generalization. Some models are more robust to scale differences, but many preprocessing benefits rely on bringing features to a common scale.

See also: normalization, standardization, min-max scaling, z-score, data preprocessing. Note that denormalization has a different meaning in database design, where it refers to reversing normalization rather than describing a feature’s scaling state.