Hacker Newsnew | past | comments | ask | show | jobs | submit | isaac_burbank's commentslogin

Two that come to mind are:

- Using data augmentation to turn the smaller amount of examples into enough samples for appropriate representation within the dataset.

- Add a weighting coefficient to the model's cost function to make misclassifying these examples more expensive.

Note: you can do serious harm to your model with either of these approaches if you don't know what you're doing. The safest solution is to collect more examples of the infrequent class.


Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: