Feature engineering is primarily about selecting the most expensive computational resources to ensure the highest model performance.

Feature engineering involves the physical construction of data centers to store the processed features securely.

Feature engineering is the process of transforming raw data into meaningful features that can improve the model's ability to predict customer churn, while ensuring compliance and scalability.

Feature engineering is equivalent to model evaluation, focusing on assessing the performance of different machine learning algorithms.

Feature engineering includes both transforming raw data into meaningful features and selecting the most relevant features to enhance model performance and efficiency.