Topic 1 Question 149
2 つ選択You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do?
Remove training examples of high-performing subgroups, and retrain the model.
Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model
Remove the features that have the highest correlations with the majority class.
Upsample or reweight your existing training data, and retrain the model
Redeploy the model, and provide a label explaining the model's behavior to users.
ユーザの投票
コメント(5)
- 正解だと思う選択肢: BD
Option B and D could be good approaches to address the issue.
B. Adding an additional objective to penalize the model more for errors made on the minority class can help the model to focus more on correctly classifying the underrepresented class.
D. Upsampling or reweighting the existing training data can help balance the class distribution and increase the model's sensitivity to the underrepresented class.
👍 3TNT872023/03/07 - 👍 2TNT872023/02/16
- 正解だと思う選択肢: BD
should be B,D
👍 2hakook2023/03/08
シャッフルモード