Comments (4)
GabrielGimenez
commented on August 20, 2024
Given the informative censoring XGBSEKaplanNeighbors and XGBSEKaplanTree modules would return biased estimatives, but XGBSEDebiasedBCE should work just fine!
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onacrame
commented on August 20, 2024
Great thanks!
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GabrielGimenez
commented on August 20, 2024
@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
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onacrame
commented on August 20, 2024
Noted thanks.
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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
- Adding DOI for Citation HOT 1
- Using a Pretrained XGBoost Model HOT 2
- How can we get the importance and output trees? HOT 1
- Compare to Actual HOT 1
- BootstrapEstimator is actually BaggingEstimator HOT 1
- Ability to pass separate training data sets in for xgboost model vs survival model HOT 1
- TypeError: predict() got an unexpected keyword argument 'iteration_range' HOT 1
- SHAP explanation for XGBSEKaplanTree or bootstrapestimator. HOT 6
- Ensembling of predictions HOT 1
- XGBSE error with sklearn pipeline and GridSearchCV HOT 2
- feature selection
- feature importance
- Category features support HOT 2
- Giving individual weights in the xgbsestackedweibull HOT 1
- AttributeError: 'XGBSEBootstrapEstimator' object has no attribute 'get_neighbors' HOT 1
- 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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