preprocessings

Preprocessings refer to the set of operations applied to raw data before analysis, modeling, or interpretation. The goal is to improve data quality, consistency, and the suitability of data representations for downstream tasks. Preprocessings are used across many domains, including machine learning, statistics, image processing, natural language processing, and signal processing.

Common preprocessings include cleaning, normalization, transformation, encoding, and reduction. Specific tasks include handling missing values (imputation or removal), removing duplicates, correcting errors, and dealing with outliers. For numerical features, scaling methods such as normalization or standardization are common. For categorical variables, encoding techniques such as one-hot or label encoding are typical. Feature engineering, dimensionality reduction, and data augmentation are also considered preprocessings in broader contexts. In image processing, preprocessing often involves resizing, color normalization, and augmentation; in text processing, it includes tokenization, lowercasing, stopword removal, stemming or lemmatization, and vectorization. Time-series or signal data may require resampling, filtering, smoothing, or alignment.

Preprocessings are usually implemented within data pipelines that apply transformations consistently to training, validation, and test data. It is important to fit transformations on the training data only to avoid data leakage, and to save the transformation parameters for reproducibility. The choice of preprocessings depends on the problem, data characteristics, and the modeling approach, and can markedly influence performance and interpretability.