Comments (4)
Given the informative censoring XGBSEKaplanNeighbors
and XGBSEKaplanTree
modules would return biased estimatives, but XGBSEDebiasedBCE
should work just fine!
from xgboost-survival-embeddings.
Great thanks!
from xgboost-survival-embeddings.
@onacrame I'm not sure the survival curve will be meaningful tho, you might have to use return_interval_probs=True
on predict and interpret your interval probabilities.
Actually, I don't know what would happen in your use case and since the XGBSEDebiasedBCE
approximate a kaplan meier it might still return biased estimations
from xgboost-survival-embeddings.
Noted thanks.
from xgboost-survival-embeddings.
Related Issues (20)
- Fitting results in AttributeError: dlsym(0x7fa409181040, XGDMatrixCreateFromDense): symbol not found
- Is it possible to recreate the xgboost object from an mlfow experiment HOT 3
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- Using a Pretrained XGBoost Model HOT 2
- How can we get the importance and output trees? HOT 1
- Compare to Actual HOT 1
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- TypeError: predict() got an unexpected keyword argument 'iteration_range' HOT 1
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- Which concordance-index is being used? HOT 1
- update pandas version requirement HOT 1
- AttributeError: `best_iteration` is only defined when early stopping is used - when xgboost>=2
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from xgboost-survival-embeddings.