Google Professional Machine Learning Engineer Question 367
Select 4Google Cloud PlatformYou are developing a machine learning model to predict customer churn for an e-commerce platform. Before deploying the model, you want to ensure that the data and model are validated to produce reliable results. Which of the following actions should you take as part of the validation process?
- A
Check for data leakage by ensuring that no future information is included in the training data.
- B
Evaluate the model only on the training dataset to confirm it has learned the patterns.
- C
Perform feature importance analysis to ensure that the model relies on relevant features.
- D
Validate the model on a test dataset that has not been seen during training.
- E
Ensure that all categorical features have been encoded consistently across training and test datasets.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Proper data and model validation is vital to ensure the reliability and accuracy of a machine learning model before deployment. Checking for data leakage, validating on unseen data, analyzing feature importance, and ensuring consistent feature encoding are all critical steps for robust validation. Skipping these steps could lead to unreliable predictions in production.
- A. Correct.
This is correct. Data leakage can lead to inflated model performance during training, but the model would fail in production when future information is unavailable.
- B. Incorrect.
This is incorrect. Evaluating the model only on the training dataset does not provide a realistic assessment of its performance on unseen data.
- C. Correct.
This is correct. Feature importance analysis helps ensure the model is using meaningful and relevant features, which improves interpretability and reliability.
- D. Correct.
This is correct. Validating the model on an unseen test dataset is crucial for assessing its generalization performance.
- E. Correct.
This is correct. Consistent encoding of categorical features ensures that the model behaves as expected during inference.