How do machine learning models benefit from feature engineering?

Feature engineering significantly enhances the model's ability to learn from the data because it involves creating and selecting the most relevant features that will be fed into the machine learning algorithms. By transforming raw data into a format that better captures the underlying patterns, feature engineering can lead to improved model accuracy and performance.

The process of feature engineering includes tasks such as extracting important variables, creating new features based on existing ones, and refining data types for more effective analysis. When relevant features are included, machine learning models can make better predictions because they have a more representative view of the data space they operate in. This, in turn, can improve the model's generalization on unseen data, ensuring that the predictions made by the model are more reliable and robust.

Other options do not capture the essence of how feature engineering directly contributes to model learning. For instance, while feature engineering may have an impact on data size or speed, its primary benefit is the improved ability of a model to discern patterns and relationships in the data. Similarly, feature engineering cannot eliminate the necessity for training data itself, as models still rely on training datasets to learn and validate their predictions.