From my experience, one of the bottlenecks when carrying out a project for supervised models is the reliability in labeled samples. It is not easy to have a team of labelers, any practical advice for making this process more efficient/reliable than doing it manually? Maybe looking at the attributes distributions per label type to find samples falling outside the assumed distributions?
Related topics
| Topic | Replies | Views | Activity | |
|---|---|---|---|---|
| Obtaining Labels for Fine-Tuning LLMs | 5 | 665 | July 14, 2023 | |
| Finding unusual events example: Why unlabeled data | 1 | 459 | July 13, 2023 | |
| Surpassing human-level performance Q | 1 | 565 | October 27, 2021 | |
| For Snorkel - if you can write a good labeling function, why do you need to train a model? | 0 | 144 | August 11, 2021 | |
| SPOILER: Question about Quiz Item in Week 1 | 3 | 1541 | October 15, 2022 |