Perfeature

Perfeature is a term used in data processing and machine learning to describe applying a transformation, normalization, or encoding to each feature of a dataset independently, rather than applying a single operation to the entire feature set. The approach emphasizes column-wise treatment, aligning transformations with the unique distribution and scale of each feature.

Common perfeature techniques include per-feature scaling (such as standardization or min-max scaling applied to each column separately), per-feature transformations (for numeric features, such as log or Box-Cox applied featurewise), and per-feature encoding for categorical variables (where encoding is performed per feature rather than globally).

Rationale and benefits: by operating on features individually, perfeature preserves the intrinsic properties of each feature, reduces scale-induced bias, and often improves numerical stability and model performance for algorithms sensitive to feature scale. It integrates naturally with pipelines that apply column-wise processing.

Limitations and considerations: perfeature methods may overlook interactions between features, which can be important for some models. Statistics used in perfeature scaling or transformation should be derived from training data only to avoid data leakage. Handling missing values and ensuring consistent transformations across training and deployment are also important.

See also: feature scaling, feature engineering, column transformer, data preprocessing, normalization.