featurepreprocessing

Feature preprocessing is the process of transforming raw data into numerical features suitable for machine learning models. It includes cleaning, encoding, scaling, and transforming features so that algorithms can learn effectively. It is distinct from feature engineering, which creates new features; preprocessing focuses on preparing existing features for modeling.

Common techniques include handling missing values through imputation (such as mean, median, or constant replacement), encoding categorical variables (one-hot, ordinal, or target encoding), and scaling numerical features (min-max normalization, z-score standardization, or robust scaling). Transformations such as logarithms or Box-Cox/Yeo-Johnson can stabilize variance and improve linearity. Dimensionality reduction (e.g., PCA) and feature selection may be used to reduce redundancy and improve model efficiency. The choice of methods often depends on the model type and data characteristics; for example, tree-based models are less sensitive to scaling, while distance-based methods typically benefit from it.

Best practices emphasize reproducibility and avoiding data leakage. Preprocessing steps should be learned on the training data only and then applied to validation or test data. For categorical features, unseen categories in evaluation data should be handled consistently with the training setup. Preprocessing is typically implemented in a pipeline to ensure all steps are applied identically during training and deployment, supporting reliable model evaluation and deployment.