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+--- 主题： behavior question #12 (/showthread.php?tid=292)
behavior question #12 - 000 - 10-10-2024
### **Behavioral Question:**
**"Tell me about a time when your machine learning model didn’t perform as expected after deployment. How did you handle the situation, and what changes did you make?"**
### **Good Example Answer:**
"While working on a fraud detection model for a financial services client, our model performed exceptionally well during development and testing, with high precision and recall metrics. However, after deploying the model into production, we began receiving feedback from the operations team that the model was generating a high number of false positives, flagging legitimate transactions as fraud. This caused frustration for both customers and internal teams.
I immediately gathered detailed logs from the production environment and compared them to our test data. One of the first things I noticed was that the distribution of transactions in the live data differed significantly from the data we used to train the model. This was likely due to changes in customer behavior and newer fraud patterns that weren’t present in the training data.
To address this, I collaborated with the data team to acquire more recent and representative data to retrain the model. I also introduced a more dynamic data pipeline that would refresh the training data periodically to keep it up-to-date with current transaction patterns. Additionally, I implemented an ensemble method combining the original model with a more interpretable rules-based system, which allowed us to catch complex fraud cases while giving the business team control over some decision-making thresholds.
Once these changes were implemented, we redeployed the model and closely monitored its performance. The false positive rate dropped by 30%, and customer complaints decreased significantly. This experience reinforced the importance of continuously monitoring and retraining models in production environments and ensuring that your training data is reflective of real-world conditions."
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### **What You Should Not Say:**
1. **"I ignored the issue, thinking it was an anomaly and would resolve itself over time."**
- Indicates a lack of ownership and failure to address production issues promptly.
2. **"I blamed the data team for providing outdated data without taking steps to resolve it myself."**
- Reflects a lack of collaboration and problem ownership, which are critical for cross-functional projects.
3. **"I made quick tweaks to the model without gathering enough data or understanding the root cause."**
- Demonstrates a short-sighted approach and failure to investigate the issue deeply, which can lead to recurring problems.
4. **"I didn’t update stakeholders or the operations team until after the issue was resolved."**
- Shows poor communication and could lead to stakeholder frustration due to lack of transparency.
5. **"I replaced the model entirely instead of trying to fix it, which delayed the project significantly."**
- Indicates a lack of problem-solving and troubleshooting skills, opting for drastic solutions instead of incremental fixes.