What is feature engineering in the context of machine learning?

Feature engineering is a critical step in the machine learning process that involves selecting, modifying, and creating features from raw data to improve the performance of a model. By carefully engineering features, practitioners aim to enhance the model's ability to learn patterns within the data, which ultimately leads to higher accuracy and better generalization on unseen data.

In this context, selecting features means identifying the most relevant variables that contribute to the predictions, while modifying features may include transforming variables, creating interaction terms, or even normalizing data to make it more suitable for the learning algorithms. This process is essential because the quality and relevance of the input features can significantly influence the efficacy of machine learning models.

The other options do not encapsulate the core of feature engineering. While cleaning data is an important preprocessing step, it is not synonymous with feature engineering itself. Tuning hyperparameters relates to optimizing the learning algorithms rather than the features used as input. Visualizing model outputs focuses on understanding the results rather than improving the data that drives the model. Hence, the emphasis on selecting and modifying features to enhance model performance captures the essence of feature engineering effectively.