MLA-C01 Question 375
Select 3A retail company has deployed a machine learning model to predict customer churn. Over time, the model's performance has degraded, and fewer customers are being correctly identified as likely to churn. Upon investigation, the data distribution in the recent customer behavior dataset has significantly shifted compared to the training dataset. What steps should the company take to address this issue?
- A
Retrain the model using the updated dataset that reflects the current customer behavior.
- B
Monitor the input features for changes and implement data quality validation checks.
- C
Deploy a more complex model architecture to handle the evolving patterns in the data.
- D
Implement automated monitoring to detect data and concept drift in real time.
- E
Reduce the size of the training dataset to avoid overfitting to historical trends.
Show answer and explanation
Correct answers: A, B, D
Explanation
Model drift occurs when the data distribution or relationships in the data change over time, leading to degraded model performance. Addressing drift involves retraining the model with updated data, implementing monitoring to detect drift early, and ensuring data quality. These steps help maintain the model's relevance and accuracy in the face of changing real-world conditions.
- A. Correct.
Retraining the model on an updated dataset that reflects the current customer behavior helps the model adapt to the new data distribution, addressing the drift issue.
- B. Correct.
Monitoring input features and implementing data quality validation checks ensures that the model is not affected by inconsistent or erroneous data, which can also contribute to drift.
- C. Incorrect.
Deploying a more complex model architecture is not necessarily the best approach for addressing drift. Drift is often caused by changes in data distribution rather than model complexity.
- D. Correct.
Automated monitoring for data and concept drift allows the company to detect changes in real time, enabling proactive actions to maintain model performance.
- E. Incorrect.
Reducing the size of the training dataset does not address drift and could result in a less robust model with poorer generalization capabilities.