Feature engineering primarily automates the model training process, reducing the need for manual intervention.

Feature engineering significantly reduces the computational requirements by eliminating the need for data preprocessing.

Feature engineering improves the model's performance and accuracy by transforming raw data into meaningful features that better represent the underlying problem to the model.

Feature engineering increases the dimensionality of the dataset by adding more features, which helps in capturing more complex patterns.

Feature engineering both improves model performance and accuracy and enhances interpretability by creating features that are easier for stakeholders to understand.