What is feature engineering in the context of machine learning?

Feature engineering is a critical aspect of the machine learning workflow that involves transforming raw data into meaningful features that can improve the performance of machine learning models. This process includes various tasks such as cleaning data, creating new features from existing ones, and selecting the most relevant features based on statistical or domain knowledge. Ultimately, the goal is to capture the underlying patterns in the data that the model can learn from, facilitating better predictions.

While selecting relevant data is important, feature engineering goes beyond just selection; it actively involves the transformation of that data to maximize its predictive power. Collecting data from various sources, although an important step in the overall data preparation process, is not specifically about modifying or enhancing that data into usable features for models. Implementing models in production pertains to deploying the trained models and operationalizing them for real-world applications, but it does not address the foundational work done in feature engineering, which is crucial before any model development can occur.