Timeline for Unbalanced multiclass data with XGBoost
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 10, 2021 at 21:32 | comment | added | Chris | @Guarav, not at all correct. The weights are not features for the model. They influence the gradients or error function only. | |
| Nov 29, 2020 at 12:07 | comment | added | Gaurav Pant | This example is a bit misleading, if you feed this data now as input to your classifier then you will get a 100% accuracy since you have already told the model what the actual classes are | |
| Sep 2, 2020 at 13:40 | comment | added | Maths12 | thanks for this, how does this affect the probability output for xgboost? i.e. when we come to optimize the loss function does the weight act on the optimizartion of the loss? | |
| Apr 10, 2019 at 18:16 | history | edited | Esmailian | CC BY-SA 4.0 |
added 1 character in body
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| Apr 10, 2019 at 18:09 | history | answered | Esmailian | CC BY-SA 4.0 |