Google Professional Machine Learning Engineer
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In the context of developing a machine learning model for a financial services company that aims to predict loan defaults with high accuracy, the team is considering the role of feature engineering. The dataset includes raw financial data such as income, credit score, loan amount, and employment history. The company emphasizes the importance of model interpretability for regulatory compliance and seeks to minimize computational costs without compromising accuracy. Given these constraints, which of the following best describes the advantage of feature engineering in this scenario? Choose the best option.
A
Feature engineering primarily automates the model training process, reducing the need for manual intervention.
B
Feature engineering significantly reduces the computational requirements by eliminating the need for data preprocessing.
C
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.
D
Feature engineering increases the dimensionality of the dataset by adding more features, which helps in capturing more complex patterns.
E
Feature engineering both improves model performance and accuracy and enhances interpretability by creating features that are easier for stakeholders to understand.