In machine learning, which process is essential for transforming raw data into features that improve model performance?

Feature engineering is a crucial process in machine learning that involves transforming raw data into meaningful features that can significantly enhance the performance of machine learning models. This process includes selecting, modifying, or creating new features from the available data to capture the underlying patterns that can improve model accuracy and effectiveness.

By focusing on feature engineering, practitioners can derive insights from the data that may not be immediately apparent, ensuring that the most relevant attributes are highlighted for the model to learn from. This can involve techniques such as binning, encoding categorical variables, creating interaction terms, or extracting date-related features, among others.

The other processes mentioned serve different roles in machine learning. Regularization addresses overfitting by adding a penalty to the model for having overly complex features but does not involve the initial transformation of data into features. Normalization adjusts the scale of feature values but does not create new features from raw data. Data augmentation is primarily a technique used to artificially expand the size of a dataset, especially for tasks like image classification, but it does not deal directly with the creation or transformation of features.