Which process ensures the consistency of text data during preprocessing?

Normalization is the process that ensures the consistency of text data during preprocessing. It involves transforming the text data into a standard format, which reduces the variability that can complicate subsequent analysis or modeling. This can include converting all text to lowercase, removing punctuation, and addressing differences in spelling or abbreviation. By standardizing the text, normalization helps to ensure that similar inputs are treated consistently, which enhances the performance of machine learning models because they can learn better patterns from uniform data.

While feature extraction, data augmentation, and model evaluation are important stages in the data processing and modeling workflow, they serve different purposes. Feature extraction focuses on identifying and representing important characteristics of the data for training models. Data augmentation refers to techniques to artificially increase the size of the training dataset, often by creating modified versions of the existing data. Model evaluation assesses how well a trained model performs on unseen data but does not directly influence the consistency of the inputs during preprocessing. Thus, normalization plays a critical role in maintaining data integrity before any model training or evaluation occurs.