Google Professional Machine Learning Engineer Question 365
Select 4Google Cloud PlatformYou are developing a machine learning model to predict customer churn for an online subscription service. During model evaluation, you observe that the model achieves high accuracy on the training dataset but performs poorly on the validation dataset. Which actions should you take to validate and improve the model's performance?
- A
Evaluate the model's performance using more appropriate metrics such as precision, recall, or F1-score.
- B
Increase the size of the training dataset by collecting more labeled data from users.
- C
Check for data leakage in the validation dataset, such as including future information that wouldn't be available during prediction.
- D
Reduce the regularization strength in the model to improve its flexibility and fit the training data better.
- E
Ensure that the training and validation datasets are properly split, maintaining temporal or logical separations if required.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
The model's poor performance on the validation dataset indicates issues like overfitting, data leakage, or improper validation strategy. Addressing these problems through better evaluation metrics, proper dataset splitting, increasing training data, and checking for data leakage will help improve the model's performance. Reducing regularization is not a valid solution in this case, as it may exacerbate overfitting issues.
- A. Correct.
Evaluating performance with more appropriate metrics (like precision, recall, or F1-score) is critical, especially if the dataset is imbalanced, as accuracy alone can be misleading.
- B. Correct.
Increasing the size of the training dataset can help improve the model's generalization by providing it with more representative examples of the data.
- C. Correct.
Data leakage can lead to overly optimistic validation results and poor real-world performance. Identifying and removing leakage is essential for reliable validation.
- D. Incorrect.
Reducing regularization strength might lead to overfitting the training data even further, which is not ideal given the current problem of poor validation performance.
- E. Correct.
Ensuring proper dataset splitting, such as maintaining logical or temporal separations, avoids contamination between training and validation data and ensures the model is evaluated realistically.