Define the term "Feature Engineering" in the context of machine learning.

Feature engineering is a critical part of the machine learning workflow that involves the process of using domain knowledge to create input variables, known as features, that enhance the performance of predictive models. This can include transforming raw data into formats that are more useful for machine learning algorithms, selecting relevant features, and constructing new features that capture important patterns in the data.

By leveraging domain expertise, practitioners can identify factors that contribute positively to model accuracy and robustness, leading to more effective predictions. This process is essential because the quality and relevance of input features often determine the success of a machine learning model, making it a fundamental component of any data science project. By improving how data is represented in models, feature engineering helps achieve better insights and optimized model outcomes.

Other options provided do not accurately encapsulate the essence of feature engineering. While automating data cleaning and creating visualizations are important in the data processing pipeline, they do not specifically involve the creation or transformation of input variables aimed at model performance, which is the heart of feature engineering.