Post-Lab

Post-Lab Conclusion: Detecting and Mitigating the Impact of Poisoned Data in the Boston Housing Dataset

Key Findings:

• Vulnerability of Machine Learning Models: The most immediately obvious element of this lab is the fact that linear regression models are highly sensitive to dataset poisoning attacks. It only took a relatively small number of added data points to completely derail the model's performance. This underscores the necessity of ensuring dataset validity.

• Baseline vs. Poisoned Performance: The original model had an MSE of 14.27. Once the poisoned data was added, however, the performance significantly dropped to an MSE of 23.49.

• Effectiveness of Anomaly Detection: Isolation Forest was, in fact, highly effective at identifying the erroneous data points and enabled us to remove them from the dataset.

• Model Recovery Post-Mitigation: Once the poisoned data had been identified and removed from the dataset, the model's MSE went back down to 14.38. This shows that while linear regression models are very sensitive to poisoned data, they are also able to recover.

Implications and Future Directions:

• Continuous Monitoring: This lab shows the necessity of continuously monitoring model performance and continually ensuring the validity of datasets.

• Enhanced Detection Mechanisms: Though the Isolation Forest algorithm employed here was effective in this instance, other forms of anomaly detection should be explored.

• User Education: It's absolutely imperative that the researchers and developers that are building and deploying these models understand the necessity for securing them from these types of attacks.