What is the primary purpose of feature engineering in machine learning?

Feature engineering plays a crucial role in the machine learning workflow because it involves the process of using domain knowledge to select, modify, or create new features from raw data that will help improve the model's performance. The primary purpose is to enhance the model's ability to learn from data by providing input features that carry more informative representations of the underlying problem.

By crafting meaningful input features, you enable the model to better capture the patterns in the data, leading to improved predictive performance. Features can take many forms, including numerical transformations, categorical encodings, and interaction terms, all tailored to facilitate the learning process. This targeted approach to feature creation can make significant differences in a model's accuracy and efficacy.

While the other options touch on aspects of model development or performance, they do not capture the essence of feature engineering's focus on enhancing the meaning and utility of input data for the model.