Google Professional Machine Learning Engineer
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In the context of developing a machine learning model for a classification problem involving time series data, your team has achieved an AUC ROC value of 99% on the training data with minimal experimentation. While this result is promising, the team is concerned about potential overfitting and data leakage. Beyond exploring advanced algorithms and hyperparameter tuning, what additional steps should be prioritized to ensure the model's robustness and generalizability to unseen data? (Choose two correct options)
A
Address data leakage by removing features that are highly correlated with the target variable.
B
Reduce model overfitting by opting for a simpler algorithm.
C
Adjust hyperparameters to lower the AUC ROC value as a strategy to combat overfitting.
D
Implement nested cross-validation during model training to mitigate data leakage.
E
Conduct feature importance analysis to identify and remove redundant features that do not contribute to the model's predictive power.