In the context of machine learning, what purpose does feature engineering serve?

Feature engineering plays a crucial role in machine learning by transforming raw data into a format that is more suitable for model training and prediction. Specifically, it involves the process of creating new variables or features from the existing raw data that can help improve the model's performance. This may include deriving new metrics, normalizing values, encoding categorical variables, and aggregating or decomposing data.

Creating new variables allows the model to better understand patterns and relationships within the data that could be missed if you only use the raw features. This step is essential because the right set of features can significantly enhance the predictive power of a model, leading to more accurate and reliable outcomes.

While the other options mention important aspects of data preparation and model evaluation, they don't capture the essence of feature engineering as it specifically pertains to the creation of new, informative features from the raw dataset.