xgb_pro: Train XGBoost Cox Model
In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
xgb_pro R Documentation
Train XGBoost Cox Model
Description
Fits an XGBoost model using the Cox proportional hazards objective function.
Usage
xgb_pro(X, y_surv, tune = FALSE)
Arguments
X
A data frame of predictors.
y_surv
A Surv object containing time and status.
tune
Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically).
Value
An object of class survival_xgboost and pro_model.
E2E documentation built on March 22, 2026, 1:07 a.m.
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| xgb_pro | R Documentation |
Train XGBoost Cox Model
Description
Fits an XGBoost model using the Cox proportional hazards objective function.
Usage
xgb_pro(X, y_surv, tune = FALSE)
Arguments
X |
A data frame of predictors. |
y_surv |
A |
tune |
Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically). |
Value
An object of class survival_xgboost and pro_model.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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