gbm_pro: Train Gradient Boosting Machine (GBM) for Survival
In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
gbm_pro R Documentation
Train Gradient Boosting Machine (GBM) for Survival
Description
Fits a stochastic gradient boosting model using the Cox Partial Likelihood distribution.
Supports random search for hyperparameter optimization.
Usage
gbm_pro(X, y_surv, tune = FALSE, cv.folds = 5, max_tune_iter = 10)
Arguments
X
A data frame of predictors.
y_surv
A Surv object.
tune
Logical. If TRUE, performs random search.
cv.folds
Integer. Number of cross-validation folds.
max_tune_iter
Integer. Maximum iterations for random search.
Value
An object of class survival_gbm and pro_model.
E2E documentation built on March 22, 2026, 1:07 a.m.
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| gbm_pro | R Documentation |
Train Gradient Boosting Machine (GBM) for Survival
Description
Fits a stochastic gradient boosting model using the Cox Partial Likelihood distribution. Supports random search for hyperparameter optimization.
Usage
gbm_pro(X, y_surv, tune = FALSE, cv.folds = 5, max_tune_iter = 10)
Arguments
X |
A data frame of predictors. |
y_surv |
A |
tune |
Logical. If TRUE, performs random search. |
cv.folds |
Integer. Number of cross-validation folds. |
max_tune_iter |
Integer. Maximum iterations for random search. |
Value
An object of class survival_gbm 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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