In the context of machine learning, what does the term 'feature engineering' refer to?

Feature engineering is a critical aspect of the machine learning workflow that involves the creation of new input features from the existing raw data. This process enhances the model's ability to learn and improve its predictive performance. By transforming raw data into a format that better captures the underlying patterns, feature engineering can involve techniques such as normalizing values, combining multiple features, and extracting relevant attributes from the data.

Creating new features allows machine learning models to gain insights that may not be apparent in the original dataset. For example, transforming categorical variables into numerical representations or generating polynomial features from continuous variables can help algorithms to more effectively capture complex patterns.

In contrast, other options focus on different stages of the machine learning process. Selecting a machine learning algorithm pertains to the phase of deciding which model to apply based on the problem and data characteristics. Evaluating model performance relates to assessing how well a model has learned from the data after training. Lastly, cleaning data involves preprocessing steps, such as addressing missing values or correcting inconsistencies, but does not encompass the generation of new features from the data itself.