3 ms·
Yeah it seems those are still kings of the hill. One newbie question maybe you can answer: Can XGboost/LightGBM handle out of band data predictions? The spec
by nlh 4y ago
Yeah it seems those are still kings of the hill.
One newbie question maybe you can answer: Can XGboost/LightGBM handle out of band data predictions? The specific regression problem I’m tackling involves price predictions on tabular data (similar the the kaggle housing price problem) and I know classic random forest / decision trees have trouble with time series predictions. Not sure if those models handle that better.
- deleted 4y ago[deleted]
- claytonjy 4y ago> classic random forest / decision trees have trouble with time series predictions This is not true. The trick is you have to convert your longitudinal data to be cross-sectional via feature engineering of lagged features. There are also related tricks like expanding datetimes into features like day of week, day of month, etc. This can be a lot of work, though there are software tools which help do this. Some general time-oriented feature engineering plus a vanilla random forest is a great second baseline (after LOCF), and then if needed you can spend 10x the time tuning a GBM to beat that. If you have a GPU, checkout fastai's tabular learning stuff; easy feature eng and neural nets with embeddings can do a lot with low effort.
- nlh 4y agoAwesome - thank you! I'll explore further.