- Unfortunately, one major take-away here isn't particularly inspiring: e.g. there's a trade-off between churn and accuracy. - Also, the thought of having to train 30-40 models to burn in in order to test this approach isn't particularly appealing. Another interesting direction for dealing with churn could be unlabelled data, or applying via constraints: e.g. if we are willing to accep X% churn, and have access to unlabeled target data, what's the best way to use that to improve the stability of our model?
- Unfortunately, one major take-away here isn't particularly inspiring: e.g. there's a trade-off between churn and accuracy.