MLS-C01 Question 87
Select 2A machine learning team at a retail company has built a model to predict customer churn. However, during evaluation, they notice that the model exhibits significant performance degradation when applied to data from a new region that was not included in the training dataset. What are the best mitigation strategies to address this issue?
- A
Increase the size of the training dataset by including more representative samples from the new region.
- B
Apply transfer learning by fine-tuning the existing model on labeled data from the new region.
- C
Use an ensemble of multiple models trained on different regions to reduce bias.
- D
Rely solely on hyperparameter tuning to improve the model's performance on the new region.
- E
Collect more diverse data from all regions and retrain the model using a feature selection approach.
Show answer and explanation
Correct answers: A, B
Explanation
Performance degradation in machine learning models when applied to new regions or domains is often caused by a shift in data distribution. The best mitigation strategies involve ensuring the training data is representative of the new region (e.g., by including more samples) or adapting the model to the new region using techniques like transfer learning. These methods address the root cause of the issue and help the model generalize better to the new data.
- A. Correct.
Including more representative samples from the new region in the training dataset helps the model generalize to the new region's data distribution, which is a key strategy for addressing performance degradation in this scenario.
- B. Correct.
Transfer learning by fine-tuning the model on labeled data from the new region can help the model adapt to the specific characteristics of the new region without requiring a complete retraining from scratch.
- C. Incorrect.
While ensemble methods can reduce bias in general, they may not directly address the issue of performance degradation caused by the new region's data distribution, making it less effective in this case.
- D. Incorrect.
Hyperparameter tuning focuses on optimizing model parameters but does not address the root cause of the issue, which is the lack of representation of the new region's data in the training set.
- E. Incorrect.
Collecting diverse data from all regions and retraining the model may improve the overall model but does not specifically mitigate the immediate performance issue for the new region. Additionally, feature selection is not directly relevant to the problem at hand.