What is a functionality of the Feature Engineering component in AutoML?

The Feature Engineering component in AutoML is crucial for enhancing the predictive power of machine learning models. This functionality focuses on creating new informative features derived from raw data, which are essential for improving the model's ability to learn patterns and make accurate predictions. By transforming and combining existing features, feature engineering can greatly influence model performance and is a key step in preparing datasets for analysis.

In contrast to shuffling data sets, which is more about ensuring the data is randomly distributed for training purposes but does not add value by creating more informative input, or visualizing data through charts, which aids in understanding data but does not directly impact the model building process, feature engineering has a direct impact on the algorithm's performance. Additionally, running performance tests on models pertains to evaluating how well a model performs after it has been created rather than modifying the data input features that feed into the model. Therefore, the emphasis on developing informative features from raw data highlights the fundamental role of feature engineering in enhancing machine learning efforts.